--- license: cc-by-4.0 language: - en pretty_name: baseline_3 v4 - DP3 Training Trajectories (DexYCB-sourced) tags: - robotics - manipulation - grasping - diffusion-policy - franka - dexycb size_categories: - n<1K --- # DP3_DexYCB_training_data 162 successful grasp + lift trajectories collected in IsaacSim 5.1 + cuRobo 0.8 from DexYCB hand-pose sequences, retargeted onto a Franka 2-finger gripper. Used to train the **baseline_3 v4** Diffusion Policy 3D (DP3) policy in the [UCB_Project](https://github.com/stzabl-png/UCB_Project) repo (`gate3-curobo-ik` branch). 10 YCB objects after dropping `foam` and `scissors` (cuRobo could not plan a single successful grasp on either shape). Each source DexYCB episode was collected at its original object yaw and one randomly-selected augmented yaw in `{90°, 180°, 270°}` around world-Z (a task-symmetric transform — gravity, table and contact geometry are unchanged by yaw rotation). > **2026-05-26 update** — A complementary OakInk-sourced dataset is now available > at [`UCBProject/DP3_OakInk_training_data`](https://huggingface.co/datasets/UCBProject/DP3_OakInk_training_data) > (207 ep, 45 obj). For the next DP3 round we will train on the **combined > 369-ep dataset** (DexYCB 162 + OakInk 207). See the > "Combined Training (DexYCB + OakInk → fresh DP3 model)" section below. ## Per-object Breakdown | ycb_class_id | object | orig | yaw aug | total | |--------------|--------------|------|---------|-------| | 03 | sugar | 14 | 8 | 22 | | 04 | tomato | 14 | 6 | 20 | | 05 | mustard | 11 | 4 | 15 | | 06 | tuna | 11 | 6 | 17 | | 07 | pudding | 9 | 9 | 18 | | 08 | gelatin | 11 | 4 | 15 | | 09 | potted_meat | 11 | 7 | 18 | | 12 | bleach | 17 | 8 | 25 | | 15 | drill | 3 | 4 | 7 | | 18 | marker | 3 | 2 | 5 | | | **TOTAL** | 104 | 58 | **162** | Total size: ~238 MB. Each `.hdf5` is ~1.5 MB. ## Per-episode Schema (HDF5) | Field | Shape | dtype | Notes | |--------------------|----------------|---------|-------| | `state` | `(31, 8)` | float32 | `[x,y,z, qw,qx,qy,qz, gripper]` in object-centric G-frame, retarget-quat convention | | `action` | `(31, 8)` | float32 | `state[1:]` (shifted by 1) | | `point_cloud` | `(31, 4096, 3)`| float32 | Static CAD surface samples in G-frame (all 31 frames identical; object is static during collection) | | `obj_origin_G` | attr `(3,)` | float64 | Object frame origin in G-frame | | `obj_quat_G_wxyz` | attr `(4,)` | float64 | Object orientation in G-frame | | `ycb_class_id` | attr scalar | int | DexYCB class id (e.g. 03 = sugar) | ## File Naming ``` dexycb________ycb_dex_NN[_yawDDD].hdf5 ``` - No `_yaw` suffix → original DexYCB yaw - `_yaw90` / `_yaw180` / `_yaw270` → yaw-augmented variant ## Download ```bash # Option 1: huggingface-cli huggingface-cli download UCBProject/DP3_DexYCB_training_data \ --repo-type dataset \ --local-dir Baseline1/data/episodes_b3_v4_full12_yaw # Option 2: snapshot_download from Python from huggingface_hub import snapshot_download snapshot_download(repo_id="UCBProject/DP3_DexYCB_training_data", repo_type="dataset", local_dir="Baseline1/data/episodes_b3_v4_full12_yaw") ``` The 162 hdf5 files land under `data/` inside this repo, so the `--local-dir` above ends up with `Baseline1/data/episodes_b3_v4_full12_yaw/data/<162 hdf5>`. For the UCB_Project pipeline, move them up one level so the path matches the README: ```bash mv Baseline1/data/episodes_b3_v4_full12_yaw/data/*.hdf5 \ Baseline1/data/episodes_b3_v4_full12_yaw/ rmdir Baseline1/data/episodes_b3_v4_full12_yaw/data ``` ## Training Pipeline Full retrain instructions (DexYCB-only, original run): [`Baseline1/RETRAIN_V4_FULL12.md`](https://github.com/stzabl-png/UCB_Project/blob/gate3-curobo-ik/Baseline1/RETRAIN_V4_FULL12.md) in the UCB_Project repo. --- ## Combined Training (DexYCB + OakInk → fresh DP3 model) We are now training a **new DP3 model** that combines this 162-ep DexYCB set with the 207-ep OakInk set at [`UCBProject/DP3_OakInk_training_data`](https://huggingface.co/datasets/UCBProject/DP3_OakInk_training_data). **Important — preserve previous DexYCB-only artefacts**: - The A6000 already has the previous DexYCB-only DP3 checkpoint (`v4_sml` experiment, 3000-epoch run) and the corresponding train/test split saved on disk. We still intend to evaluate that model. **The new combined run MUST use distinct output paths so nothing is overwritten.** - **Sim collection** for this round was completed entirely on the dev box (RTX 5090). The earlier plan to also run sim collection on A6000 was abandoned because the system glibc (2.31) is incompatible with IsaacSim 5.1's requirement (glibc 2.35). **The A6000 is training-only this round.** (See [`UCBProject/baseline_3_v4_collection_assets`](https://huggingface.co/datasets/UCBProject/baseline_3_v4_collection_assets) for the deprecated A6000 collection instructions, kept for reference only.) ### Step 1 — Layout the combined dataset in a fresh dir ```bash cd $HOME/UCB_Project # the A6000 repo clone # Fresh dir — do NOT reuse Baseline1/data/episodes_b3_v4_full12_yaw which holds # the 162-ep DexYCB set and is the training input for the existing model. NEW=Baseline1/data/episodes_b3_v4_dexycb162_oakink207_2026-05-26 mkdir -p "$NEW" # 1.1 Copy DexYCB 162 ep from the existing local dir (already downloaded — # do NOT re-download). cp Baseline1/data/episodes_b3_v4_full12_yaw/*.hdf5 "$NEW/" # 1.2 Download the new OakInk 207 ep huggingface-cli download UCBProject/DP3_OakInk_training_data \ --repo-type dataset --local-dir /tmp/oakink_dl --include "data/*.hdf5" cp /tmp/oakink_dl/data/*.hdf5 "$NEW/" # 1.3 Verify count ls "$NEW"/*.hdf5 | wc -l # expect 162 + 207 = 369 ``` ### Step 2 — Build a FRESH zarr (do not overwrite the existing one) ```bash conda activate dp3 # same env A6000 already has python Baseline1/convert_to_zarr.py \ "$NEW" \ --output_zarr Baseline1/data/dp3_train_v4_dexycb162_oakink207.zarr ``` Existing zarr (DexYCB-only) at `Baseline1/data/dp3_train_v4_sml.zarr` remains untouched. ### Step 3 — Fresh train/test split The previous split lives in `third_party/3D-Diffusion-Policy/.../experiments/v4_sml/data_split/`. **Do not touch it.** Make a new experiment dir: ```bash cd third_party/3D-Diffusion-Policy/3D-Diffusion-Policy EXP=dexycb162_oakink207_2026-05-26 python ../../../Baseline1/split_v4_full12.py \ --zarr ../../../Baseline1/data/dp3_train_v4_dexycb162_oakink207.zarr \ --train_ratio 0.8 \ --out_dir experiments/$EXP/data_split ``` ### Step 4 — Fresh DP3 config + output dir Copy the prior config and adjust: ```bash cp config/v4_sml.yaml config/${EXP}.yaml # Edit config/${EXP}.yaml: # task.dataset_zarr_path: Baseline1/data/dp3_train_v4_dexycb162_oakink207.zarr # exp_name: ${EXP} # hydra.run.dir: experiments/${EXP}/${now:%Y-%m-%d_%H-%M-%S} # checkpoint.save_ckpt: True ← critical, defaulted False historically # checkpoint.topk.k: 3 (or more) # training.num_epochs: 3000 ``` ### Step 5 — Launch ```bash WANDB_MODE=online # or offline if A6000 has no internet python train.py --config-name=${EXP} ``` Expected wall time on A6000 at batch_size=128: ~6 h for 3000 epochs on 369 ep (vs ~3 h for 162 ep). ### Step 6 — Output lands in a fresh dir ``` experiments/${EXP}/{date_time}/checkpoints/ experiments/${EXP}/{date_time}/wandb/ ``` The previous experiment (`experiments/v4_sml/`) and its checkpoint remain untouched. To re-evaluate the previous model later: ```bash python eval.py --config-name=v4_sml # unchanged from before ``` --- ## Collection Details Collected by `sim/run_grasp_sim_baseline3_v4.py` (`gate3-curobo-ik` branch) with: - IsaacSim 5.1, PhysX TGS solver, GPU dynamics + CCD - cuRobo 0.8 motion planner (per-phase mesh toggle for pre-grasp WITH mesh, final/lift WITHOUT mesh) - mass = 0.05 kg (hardcoded; real per-class mass triggers PhysX overflow) - chunked-5 + retry wrapper to recover from PhysX corruption events ## License Data: CC-BY-4.0. DexYCB source data subject to the original DexYCB license. ## Citation If you use this dataset, please also cite DexYCB: ``` @inproceedings{chao2021dexycb, title = {DexYCB: A Benchmark for Capturing Hand Grasping of Objects}, author = {Chao, Yu-Wei and Yang, Wei and Xiang, Yu and Molchanov, Pavlo and Handa, Ankur and Tremblay, Jonathan and Narang, Yashraj S and Van Wyk, Karl and Iqbal, Umar and Birchfield, Stan and others}, booktitle = {CVPR}, year = {2021} } ```