metadata
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
This dataset was created using LeRobot.
Dataset Description
Homepage: 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.
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- Important Notes:
- This is a G1 diversity dataset that can be used for video generation models, world models, and other applications [Lee et al., 2018].
- If you want to use the lerobotv2.1 format, refer to this file for conversion: convert_v3_to_v2.py
- 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.
- 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
{
"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:
@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}
}