stanford_hydra_dataset_converted_externally_to_rlds

FeaturesDict({
    'episode_metadata': FeaturesDict({
        'file_path': Text(shape=(), dtype=string),
    }),
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float32, description=Robot action, consists of [3x EEF positional delta, 3x EEF orientation delta in euler angle, 1x close gripper].),
        'discount': Scalar(shape=(), dtype=float32, description=Discount if provided, default to 1.),
        'is_dense': Scalar(shape=(), dtype=bool, description=True if state is a waypoint(010) or in dense mode(x111).),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'language_embedding': Tensor(shape=(512,), dtype=float32, description=Kona language embedding. See https://tfhub.dev/google/universal-sentence-encoder-large/5),
        'language_instruction': Text(shape=(), dtype=string),
        'observation': FeaturesDict({
            'image': Image(shape=(240, 320, 3), dtype=uint8, description=Main camera RGB observation.),
            'state': Tensor(shape=(27,), dtype=float32, description=Robot state, consists of [3x EEF position,4x EEF orientation in quaternion,3x EEF orientation in euler angle,7x robot joint angles, 7x robot joint velocities,3x gripper state.),
            'wrist_image': Image(shape=(240, 320, 3), dtype=uint8, description=Wrist camera RGB observation.),
        }),
        'reward': Scalar(shape=(), dtype=float32, description=Reward if provided, 1 on final step for demos.),
    }),
})

@article{belkhale2023hydra,
 title={HYDRA: Hybrid Robot Actions for Imitation Learning},
 author={Belkhale, Suneel and Cui, Yuchen and Sadigh, Dorsa},
 journal={arxiv},
 year={2023}
}