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Aren99/G1_Dex1_DiverseManip_DualArm_128x128

This dataset was created using LeRobot. 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… See the full description on the dataset page: https://huggingface.co/datasets/Aren99/G1_Dex1_DiverseManip_DualArm_128x128.

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face
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Dataset Card

This dataset was created using LeRobot.

Dataset Description

  • —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.

<table> <tr> <td><img src="assets/4.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:
  • —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

meta/info.json:

json
{
    "codebase_version": "v3.0",
    "robot_type": "Unitree_G1_Dex1",
    "total_episodes": 525,
    "total_frames": 413538,
    "total_tasks": 1,
    "chunks_size": 1000,
    "data_files_size_in_mb": 100,
    "video_files_size_in_mb": 500,
    "fps": 30,
    "splits": {
        "train": "0:525"
    },
    "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": "av1",
                "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": "av1",
                "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}
}