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--- |
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license: apache-2.0 |
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task_categories: |
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- robotics |
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tags: |
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- LeRobot |
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configs: |
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- config_name: default |
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data_files: data/*/*.parquet |
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--- |
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This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). |
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## Dataset Description |
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This dataset contains the final batch of teleoperated demonstrations collected during a two-day hackathon using the LeRobot library and SO-101 robot arms in a leader–follower configuration. |
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Each episode shows the follower arm picking two colored cubes (one after the other) and placing each into the matching colored cross within a 2×2 grid. Two RGB cameras were used: |
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Top camera: mounted above the workspace for a clear 2D view of the arm, cubes, and grid. |
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Front/low camera: slightly above the ground, facing the arm and grid to provide better z-axis cues and arm self-perception. |
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Despite cardboard backgrounding, the room’s illumination varied over time and is deliberately preserved in the data, as it proved to be a limiting factor and may be valuable for robustness research. |
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This dataset is intended for vision-based imitation learning (e.g., behavior cloning, goal-conditioned policies), multi-view fusion, and perception-control studies on tabletop manipulation. |
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### Use Cases |
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- **Imitation Learning**: Behavior cloning from teleop demonstrations. |
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- **Multiview Perception**: Fusing top + front perspectives for depth inference without explicit depth sensors. |
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- **Robustness to Lighting**: Evaluating policy sensitivity to illumination drift. |
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- **State–Action Alignment**: Leveraging synchronized proprioception and images. |
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## Data Collection |
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### Teleoperation Setup |
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- **Leader–Follower**: Human teleoperates a leader arm; follower SO-101 replicates motion to generate demonstrations. |
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- **Workspace**: Tabletop with a 2×2 grid. Each cell contains a colored cross; two colored cubes must be placed on matching crosses. |
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- **Cameras**: |
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- **Top**: overhead, full scene. |
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- **Front**: low angle, emphasizes depth and arm self-pose. |
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- **Background control**: Cardboard panels; lighting varies during the day and is preserved in data. |
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### Episode Protocol |
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1- Move to pre-grasp; localize target cube(s) visually. |
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2- Grasp first cube; transport; place on correct colored cross. |
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3- Repeat for second cube. |
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4- Return to neutral. |
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## Known limitations |
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- **Lighting drift**: Significant variation during the day; expect distribution shift. Consider color constancy or data augmentation. |
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- **Camera motion**: Cameras are fixed for the batch, but small nudges may occur; rely on metadata intrinsics/extrinsics if provided. |
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- **Occlusions**: Self-occlusion of the gripper and cubes in certain positions, especially from left camera during close approach. |
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- **No depth**: RGB only |
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## Additional Information |
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- **Homepage:** [deel-ai](https://www.irt-saintexupery.com/deel/) |
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- **License:** apache-2.0 |
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## Dataset Structure |
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[meta/info.json](meta/info.json): |
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```json |
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{ |
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"codebase_version": "v3.0", |
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"robot_type": "so101_follower", |
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"total_episodes": 50, |
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"total_frames": 31189, |
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"total_tasks": 1, |
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"chunks_size": 1000, |
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"data_files_size_in_mb": 100, |
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"video_files_size_in_mb": 500, |
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"fps": 30, |
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"splits": { |
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"train": "0:50" |
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}, |
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"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet", |
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"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4", |
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"features": { |
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"action": { |
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"dtype": "float32", |
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"names": [ |
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"shoulder_pan.pos", |
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"shoulder_lift.pos", |
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"elbow_flex.pos", |
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"wrist_flex.pos", |
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"wrist_roll.pos", |
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"gripper.pos" |
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], |
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"shape": [ |
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6 |
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] |
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}, |
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"observation.state": { |
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"dtype": "float32", |
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"names": [ |
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"shoulder_pan.pos", |
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"shoulder_lift.pos", |
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"elbow_flex.pos", |
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"wrist_flex.pos", |
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"wrist_roll.pos", |
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"gripper.pos" |
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], |
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"shape": [ |
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6 |
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] |
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}, |
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"observation.images.left": { |
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"dtype": "video", |
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"shape": [ |
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480, |
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640, |
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3 |
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], |
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"names": [ |
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"height", |
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"width", |
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"channels" |
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], |
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"info": { |
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"video.height": 480, |
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"video.width": 640, |
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"video.codec": "av1", |
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"video.pix_fmt": "yuv420p", |
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"video.is_depth_map": false, |
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"video.fps": 30, |
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"video.channels": 3, |
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"has_audio": false |
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} |
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}, |
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"observation.images.front": { |
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"dtype": "video", |
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"shape": [ |
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480, |
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640, |
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3 |
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], |
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"names": [ |
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"height", |
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"width", |
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"channels" |
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], |
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"info": { |
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"video.height": 480, |
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"video.width": 640, |
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"video.codec": "av1", |
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"video.pix_fmt": "yuv420p", |
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"video.is_depth_map": false, |
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"video.fps": 30, |
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"video.channels": 3, |
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"has_audio": false |
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} |
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}, |
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"timestamp": { |
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"dtype": "float32", |
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"shape": [ |
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1 |
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], |
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"names": null |
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}, |
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"frame_index": { |
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"dtype": "int64", |
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"shape": [ |
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1 |
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], |
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"names": null |
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}, |
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"episode_index": { |
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"dtype": "int64", |
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"shape": [ |
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1 |
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], |
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"names": null |
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}, |
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"index": { |
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"dtype": "int64", |
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"shape": [ |
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1 |
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], |
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"names": null |
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}, |
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"task_index": { |
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"dtype": "int64", |
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"shape": [ |
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1 |
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], |
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|
"names": null |
|
|
} |
|
|
} |
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} |
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``` |
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