berkeley_cable_routing

• Description:

Routing cable into clamps on table top

• Homepage: https://sites.google.com/view/cablerouting/home

• Source code: tfds.robotics.rtx.BerkeleyCableRouting

• Versions:

  • 0.1.0 (default): Initial release.

• Download size: Unknown size

• Dataset size: 4.67 GiB

• Auto-cached (documentation): No

• Splits:

• Feature structure:

FeaturesDict({
    'steps': Dataset({
        'action': FeaturesDict({
            'rotation_delta': Tensor(shape=(3,), dtype=float32),
            'terminate_episode': float32,
            'world_vector': Tensor(shape=(3,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'image': Image(shape=(128, 128, 3), dtype=uint8),
            'natural_language_embedding': Tensor(shape=(512,), dtype=float32),
            'natural_language_instruction': string,
            'robot_state': Tensor(shape=(7,), dtype=float32),
            'top_image': Image(shape=(128, 128, 3), dtype=uint8),
            'wrist225_image': Image(shape=(128, 128, 3), dtype=uint8),
            'wrist45_image': Image(shape=(128, 128, 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{luo2023multistage,
  author    = {Jianlan Luo and Charles Xu and Xinyang Geng and Gilbert Feng and Kuan Fang and Liam Tan and Stefan Schaal and Sergey Levine},
  title     = {Multi-Stage Cable Routing through Hierarchical Imitation Learning},
  journal   = {arXiv pre-print},
  year      = {2023},
  url       = {https://arxiv.org/abs/2307.08927},
}