| --- |
| title: EXYLOS |
| emoji: 🤖 |
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| <p align="center"> |
| <h1 align="center">EXYLOS</h1> |
| <p align="center"><strong>Robot-ready skill datasets for manipulation policy learning and evaluation.</strong></p> |
| </p> |
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| --- |
|
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| ## What we do |
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| EXYLOS builds structured robot manipulation datasets for imitation learning, VLA models, policy training, and evaluation. |
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| Raw videos show what happened, but policy learning also needs synchronized actions, states, task metadata, outcomes, and failure context. EXYLOS turns human-seeded manipulation workflows performed in our simulations into train-ready episodes with multi-view observations, trajectories, annotations, success/failure labels, and quality diagnostics. |
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| --- |
|
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| ## Public sample datasets |
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| This organization hosts compact inspection samples for checking schema, loading data, inspecting trajectories, and evaluating whether the EXYLOS format fits a robotics ML stack. |
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| | Dataset | Status | Contents | |
| |---|---:|---| |
| | [`ExylosAi/pick_and_place_sample`](https://huggingface.co/datasets/ExylosAi/pick_and_place_sample) | Available | 50 pick-and-place episodes, 21,412 frames, 5 RGB views, 9D Panda state/action, 30 success and 20 failure episodes. | |
| | [`ExylosAi/bimanual_table_spill_cleanup`](https://huggingface.co/datasets/ExylosAi/table_spill_cleanup_bimanual) | Available | 50 bimanual spill-cleanup episodes, 67,742 frames, 6 RGB views, 18D dual-Panda state/action, 35 success and 15 failure episodes. | |
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| More contact-rich and recovery-heavy samples are planned. |
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| --- |
|
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| ## Dataset format |
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| EXYLOS samples are packaged to be compatible with the LeRobot ecosystem whenever possible. A typical dataset contains: |
|
|
| ```text |
| README.md |
| LICENSE.txt |
| annotations.json |
| meta/ |
| info.json |
| tasks.jsonl |
| episodes.jsonl |
| episodes_stats.jsonl |
| data/ |
| chunk-000/ |
| episode_000000.parquet |
| episode_000001.parquet |
| videos/ |
| chunk-000/ |
| observation.images.<camera_name>/ |
| episode_000000.mp4 |
| episode_000001.mp4 |
| ``` |
|
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| Core signals: |
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| | Category | Examples | |
| |---|---| |
| | Visual observations | Synchronized RGB wrist and scene views | |
| | Action and state | Robot state, action vectors, timestamps, frame indices | |
| | Labels | Success, failure reason, terminal flags, collisions, aborts, retries | |
| | Annotations | Phase boundaries, hand labels, object notes, scores, derived metrics | |
| | Metadata | Task description, duration, splits, feature schema, validation stats | |
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| Exact fields vary by dataset, so each repository includes a dataset-specific card. |
|
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| --- |
|
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| ## Why EXYLOS datasets are different |
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| - **Structured, not raw:** episodes include synchronized video, actions, state, metadata, annotations, and quality checks. |
| - **Skill-oriented:** each dataset is organized around a manipulation workflow rather than unrelated clips. |
| - **Failure-aware:** samples include failed attempts, aborts, collisions, incomplete task executions, and recovery-relevant labels when available. |
| - **LeRobot-oriented:** data is stored in open formats such as MP4, Parquet, JSON, and JSONL. |
| - **Transfer-minded:** workflows are captured from human intent in consumer VR and procedurally expanded, with added visual domain randomization for broader policy-learning experiments. |
|
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| --- |
|
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| ## Intended use |
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| Public samples are suitable for: |
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| - inspecting EXYLOS schema and annotation conventions |
| - testing LeRobot-compatible loaders and training pipelines |
| - running small imitation-learning experiments |
| - reviewing multi-view video, trajectories, and phase annotations |
| - evaluating whether a custom EXYLOS skill pack would fit your workflow |
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| They are compact inspection datasets, not complete production-scale benchmarks. |
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| --- |
|
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| ## Commercial datasets and custom skill packs |
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| EXYLOS can generate custom robot-ready skill datasets for pick-and-place, bimanual manipulation, spill cleanup, sorting, binning, object rearrangement, failure recovery, and evaluation/regression sets. |
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| Commercial deliveries can add depth, segmentation masks, object states, event labels, custom cameras, larger episode volumes, stricter QA, and internal-pipeline packaging. |
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| --- |
|
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| ## License |
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| Current public samples are released under Apache 2.0. Please check each dataset card and license file before using a sample in research, demos, training, or commercial workflows. |
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| --- |
|
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| ## Contact |
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| - Website: [exylos.ai](https://exylos.ai) |
| - Email: contact@exylos.ai |
| - LinkedIn: [Exylos on LinkedIn](https://www.linkedin.com/company/exylos-ai/) |
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| If you need structured skill data, send us the target task, robot, modalities, format, evaluation criteria, and timeline. |
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