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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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- gaze |
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- foveated-vision |
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- robot-learning |
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- simulation |
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library_name: 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, presented in the paper [Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers](https://huggingface.co/papers/2507.15833), provides a simulation benchmark and dataset for training robot policies that incorporate human gaze. It includes bimanual robot demonstrations with synchronized human eye-tracking data collected using the AV-ALOHA simulation platform for the peg insertion task. This dataset is part of a larger effort to explore how human-like active gaze can enhance robot learning efficiency and robustness. |
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- **Homepage:** [https://ian-chuang.github.io/gaze-av-aloha/](https://ian-chuang.github.io/gaze-av-aloha/) |
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- **Paper:** [https://huggingface.co/papers/2507.15833](https://huggingface.co/papers/2507.15833) |
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- **Code:** [https://github.com/ian-chuang/gaze-av-aloha](https://github.com/ian-chuang/gaze-av-aloha) |
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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": "v2.1", |
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"robot_type": null, |
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"total_episodes": 100, |
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"total_frames": 17741, |
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"total_tasks": 1, |
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"total_videos": 600, |
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"total_chunks": 1, |
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"chunks_size": 1000, |
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"fps": 25, |
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"splits": { |
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"train": "0:100" |
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}, |
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"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", |
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"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", |
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"features": { |
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"observation.images.zed_cam_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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"channel" |
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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": 25, |
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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.zed_cam_right": { |
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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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"channel" |
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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": 25, |
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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.wrist_cam_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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"channel" |
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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": 25, |
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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.wrist_cam_right\": { |
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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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\"channel\" |
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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\": 25, |
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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.overhead_cam\": { |
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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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\"channel\" |
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], |
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\"info\": { |
|
|
\"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\": 25, |
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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.worms_eye_cam\": { |
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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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], |
|
|
\"names\": [ |
|
|
\"height\", |
|
|
\"width\", |
|
|
\"channel\" |
|
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], |
|
|
\"info\": { |
|
|
\"video.height\": 480, |
|
|
\"video.width\": 640, |
|
|
\"video.codec\": \"av1\", |
|
|
\"video.pix_fmt\": \"yuv420p\", |
|
|
\"video.is_depth_map\": false, |
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|
\"video.fps\": 25, |
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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.state\": { |
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"dtype": "float32", |
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"shape": [ |
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21 |
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], |
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"names": null |
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}, |
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"observation.environment_state": { |
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"dtype": "float32", |
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"shape": [ |
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14 |
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], |
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"names": null |
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}, |
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"action": { |
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"dtype": "float32", |
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"shape": [ |
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21 |
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], |
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"names": null |
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}, |
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"left_eye": { |
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"dtype": "float32", |
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"shape": [ |
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2 |
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], |
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"names": null |
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}, |
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"right_eye": { |
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"dtype": "float32", |
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"shape": [ |
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2 |
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], |
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"names": null |
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}, |
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"left_arm_pose": { |
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"dtype": "float32", |
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"shape": [ |
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16 |
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], |
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"names": null |
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}, |
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"right_arm_pose": { |
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"dtype": "float32", |
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"shape": [ |
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16 |
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], |
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"names": null |
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}, |
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"middle_arm_pose": { |
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"dtype": "float32", |
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"shape": [ |
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16 |
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], |
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"names": null |
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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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} |
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``` |
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## Sample Usage |
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This dataset is provided in LeRobot format for ease of sharing and visualization. For faster access during training, it is recommended to convert the dataset to a custom `AVAlohaDataset` format based on Zarr. |
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1. **Install Dependencies:** |
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First, ensure you have the `lerobot` library and other necessary dependencies installed as described in the official GitHub repository. |
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```bash |
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# Install LeRobot (if not already installed) |
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pip install git+https://github.com/huggingface/lerobot.git |
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# Clone the gaze-av-aloha repository for scripts and set up environment |
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git clone https://github.com/ian-chuang/gaze-av-aloha.git |
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cd gaze-av-aloha |
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# Follow additional installation steps from the repo's README, e.g., conda env setup |
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conda create -n gaze python=3.10 |
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conda activate gaze |
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pip install -e ./gym_av_aloha |
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pip install -e ./gaze_av_aloha |
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``` |
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2. **Convert Dataset to Zarr Format:** |
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Use the conversion script provided in the GitHub repository to convert this dataset to the Zarr format: |
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```bash |
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python gym_av_aloha/scripts/convert_lerobot_to_avaloha.py --repo_id iantc104/av_aloha_sim_peg_insertion |
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``` |
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Converted datasets will be saved under `gym_av_aloha/outputs/`. |
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For more detailed usage, including training and evaluating policies, please refer to the [project's GitHub repository](https://github.com/ian-chuang/gaze-av-aloha). |
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## Citation |
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**BibTeX:** |
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```bibtex |
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@misc{chuang2025lookfocusactefficient, |
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title={Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers}, |
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author={Ian Chuang and Andrew Lee and Dechen Gao and Jinyu Zou and Iman Soltani}, |
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year={2025}, |
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eprint={2507.15833}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.RO}, |
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url={https://arxiv.org/abs/2507.15833}, |
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} |
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``` |
