bridge

• Description:

WidowX interacting with toy kitchens

• Homepage: https://rail-berkeley.github.io/bridgedata/

• Source code: tfds.robotics.rtx.Bridge

• Versions:

  • 0.1.0 (default): Initial release.

• Download size: Unknown size

• Dataset size: 387.49 GiB

• Auto-cached (documentation): No

• Splits:

• Feature structure:

FeaturesDict({
    'episode_metadata': FeaturesDict({
        'episode_id': Scalar(shape=(), dtype=int32),
        'file_path': string,
        'has_image_0': Scalar(shape=(), dtype=bool),
        'has_image_1': Scalar(shape=(), dtype=bool),
        'has_image_2': Scalar(shape=(), dtype=bool),
        'has_image_3': Scalar(shape=(), dtype=bool),
        'has_language': Scalar(shape=(), dtype=bool),
    }),
    '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_0': Image(shape=(256, 256, 3), dtype=uint8),
            'image_1': Image(shape=(256, 256, 3), dtype=uint8),
            'image_2': Image(shape=(256, 256, 3), dtype=uint8),
            'image_3': Image(shape=(256, 256, 3), dtype=uint8),
            'state': Tensor(shape=(7,), dtype=float32),
        }),
        '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:

@inproceedings{walke2023bridgedata,
    title={BridgeData V2: A Dataset for Robot Learning at Scale},
    author={Walke, Homer and Black, Kevin and Lee, Abraham and Kim, Moo Jin and Du, Max and Zheng, Chongyi and Zhao, Tony and Hansen-Estruch, Philippe and Vuong, Quan and He, Andre and Myers, Vivek and Fang, Kuan and Finn, Chelsea and Levine, Sergey},
    booktitle={Conference on Robot Learning (CoRL)},
    year={2023}
}