| --- |
| license: apache-2.0 |
| task_categories: |
| - robotics |
| tags: |
| - LeRobot |
| configs: |
| - config_name: default |
| data_files: data/*/*.parquet |
| --- |
| |
| This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). |
|
|
| ## Dataset Description |
|
|
| - **Homepage:** [unitreerobotics](https://github.com/unitreerobotics) |
| - **License:** apache-2.0 |
|
|
|
|
| - **Task Objective:** Organize and tidy the items on the table. |
| - **Operation Duration:** Each operation takes approximately 20 to 40 seconds. |
| - **Recording Frequency:** 30 Hz. |
| - **Robot Type:** 7-DOF dual-arm G1 robot. |
| - **End Effector:** Gripper. |
| - **Dual-Arm Operation:** Yes. |
| - **Image Resolution:** 128x128. |
| - **Camera Positions:** head-mounted (binocular cameras). |
| - **Data Content:** |
| • Robot's current state. |
| • Robot's next action. |
| • Current camera view images. |
| - **Robot Initial Posture:** The first robot state in each dataset entry. |
| - **Object Placement:** Randomly placed within the robot arm's motion range and the field of view of the robot's head-mounted camera. |
| - **Camera View:** Follow the guidelines in **Part 5** of [AVP Teleoperation Documentation](https://github.com/unitreerobotics/avp_teleoperate). |
|
|
| <table> |
| <tr> |
| <td><img src="assets/0.gif" width="200px" /></td> |
| <td><img src="assets/1.gif" width="200px" /></td> |
| <td><img src="assets/2.gif" width="200px" /></td> |
| <td><img src="assets/3.gif" width="200px" /></td> |
| </tr> |
| </table> |
| |
|
|
| - **Important Notes:** |
| 1. This is a G1 diversity dataset that can be used for video generation models, world models, and other applications \[[Lee et al., 2018](#citation)\]. |
| 2. If you want to use the lerobotv2.1 format, refer to this file for conversion: [convert_v3_to_v2.py](https://github.com/NVIDIA/Isaac-GR00T/blob/main/scripts/lerobot_conversion/convert_v3_to_v2.py) |
| 3. Due to the inability to precisely describe spatial positions, adjust the scene to closely match the first frame of the dataset after installing the hardware as specified in **Part 5** of [AVP Teleoperation Documentation](https://github.com/unitreerobotics/avp_teleoperate). |
| 4. Data collection is not completed in a single session, and variations between data entries exist. Ensure these variations are accounted for during model training. |
|
|
| ## Dataset Structure |
|
|
| [meta/info.json](meta/info.json): |
| ```json |
| { |
| "codebase_version": "v3.0", |
| "robot_type": "Unitree_G1_Dex1", |
| "total_episodes": 468, |
| "total_frames": 331555, |
| "total_tasks": 1, |
| "chunks_size": 1000, |
| "data_files_size_in_mb": 100, |
| "video_files_size_in_mb": 500, |
| "fps": 30, |
| "splits": { |
| "train": "0:468" |
| }, |
| "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet", |
| "video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4", |
| "features": { |
| "observation.state": { |
| "dtype": "float32", |
| "shape": [ |
| 16 |
| ], |
| "names": [ |
| [ |
| "kLeftShoulderPitch", |
| "kLeftShoulderRoll", |
| "kLeftShoulderYaw", |
| "kLeftElbow", |
| "kLeftWristRoll", |
| "kLeftWristPitch", |
| "kLeftWristYaw", |
| "kRightShoulderPitch", |
| "kRightShoulderRoll", |
| "kRightShoulderYaw", |
| "kRightElbow", |
| "kRightWristRoll", |
| "kRightWristPitch", |
| "kRightWristYaw", |
| "kLeftGripper", |
| "kRightGripper" |
| ] |
| ] |
| }, |
| "action": { |
| "dtype": "float32", |
| "shape": [ |
| 16 |
| ], |
| "names": [ |
| [ |
| "kLeftShoulderPitch", |
| "kLeftShoulderRoll", |
| "kLeftShoulderYaw", |
| "kLeftElbow", |
| "kLeftWristRoll", |
| "kLeftWristPitch", |
| "kLeftWristYaw", |
| "kRightShoulderPitch", |
| "kRightShoulderRoll", |
| "kRightShoulderYaw", |
| "kRightElbow", |
| "kRightWristRoll", |
| "kRightWristPitch", |
| "kRightWristYaw", |
| "kLeftGripper", |
| "kRightGripper" |
| ] |
| ] |
| }, |
| "observation.images.cam_left_high": { |
| "dtype": "video", |
| "shape": [ |
| 128, |
| 128, |
| 3 |
| ], |
| "names": [ |
| "height", |
| "width", |
| "channel" |
| ], |
| "info": { |
| "video.height": 128, |
| "video.width": 128, |
| "video.codec": "h264", |
| "video.pix_fmt": "yuv420p", |
| "video.is_depth_map": false, |
| "video.fps": 30, |
| "video.channels": 3, |
| "has_audio": false |
| } |
| }, |
| "observation.images.cam_right_high": { |
| "dtype": "video", |
| "shape": [ |
| 128, |
| 128, |
| 3 |
| ], |
| "names": [ |
| "height", |
| "width", |
| "channel" |
| ], |
| "info": { |
| "video.height": 128, |
| "video.width": 128, |
| "video.codec": "h264", |
| "video.pix_fmt": "yuv420p", |
| "video.is_depth_map": false, |
| "video.fps": 30, |
| "video.channels": 3, |
| "has_audio": false |
| } |
| }, |
| "timestamp": { |
| "dtype": "float32", |
| "shape": [ |
| 1 |
| ], |
| "names": null |
| }, |
| "frame_index": { |
| "dtype": "int64", |
| "shape": [ |
| 1 |
| ], |
| "names": null |
| }, |
| "episode_index": { |
| "dtype": "int64", |
| "shape": [ |
| 1 |
| ], |
| "names": null |
| }, |
| "index": { |
| "dtype": "int64", |
| "shape": [ |
| 1 |
| ], |
| "names": null |
| }, |
| "task_index": { |
| "dtype": "int64", |
| "shape": [ |
| 1 |
| ], |
| "names": null |
| } |
| } |
| } |
| ``` |
|
|
|
|
| ## Citation |
|
|
| **BibTeX:** |
|
|
| ```bibtex |
| @article{lee2018stochastic, |
| title={Stochastic Adversarial Video Prediction}, |
| author={Lee, Alex X. and Zhang, Richard and Ebert, Frederik and Abbeel, Pieter and Finn, Chelsea and Levine, Sergey}, |
| journal={arXiv preprint arXiv:1804.01523}, |
| year={2018}, |
| url={https://arxiv.org/abs/1804.01523} |
| } |
| ``` |
