mimic_play

• Description:

Real dataset of 14 long horizon manipulation tasks. A mix of human play data and single robot arm data performing the same tasks.

• Homepage: https://mimic-play.github.io/

• Source code: tfds.robotics.rtx.MimicPlay

• Versions:

  • 0.1.0 (default): Initial release.

• Download size: Unknown size

• Dataset size: 7.14 GiB

• Auto-cached (documentation): No

• Splits:

• Feature structure:

FeaturesDict({
    'episode_metadata': FeaturesDict({
        'file_path': string,
    }),
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float32),
        'discount': Scalar(shape=(), dtype=float32),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'language_embedding': Tensor(shape=(512,), dtype=float32),
        'language_instruction': string,
        'observation': FeaturesDict({
            'image': FeaturesDict({
                'front_image_1': Image(shape=(120, 120, 3), dtype=uint8),
                'front_image_2': Image(shape=(120, 120, 3), dtype=uint8),
            }),
            'state': FeaturesDict({
                'ee_pose': Tensor(shape=(7,), dtype=float32),
                'gripper_position': float32,
                'joint_positions': Tensor(shape=(7,), dtype=float32),
                'joint_velocities': Tensor(shape=(7,), dtype=float32),
            }),
            'wrist_image': FeaturesDict({
                'wrist_image': Image(shape=(120, 120, 3), dtype=uint8),
            }),
        }),
        'reward': Scalar(shape=(), dtype=float32),
    }),
})

• Feature documentation:

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@article{wang2023mimicplay,title={Mimicplay: Long-horizon imitation learning by watching human play},author={Wang, Chen and Fan, Linxi and Sun, Jiankai and Zhang, Ruohan and Fei-Fei, Li and Xu, Danfei and Zhu, Yuke and Anandkumar, Anima},journal={arXiv preprint arXiv:2302.12422},year={2023} }