utokyo_xarm_pick_and_place_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 position, 3x EEF orientation yaw/pitch/roll, 1x gripper open/close position].),
        'discount': Scalar(shape=(), dtype=float32, description=Discount if provided, default to 1.),
        '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({
            'end_effector_pose': Tensor(shape=(6,), dtype=float32, description=Robot end effector pose, consists of [3x EEF position, 3x EEF orientation yaw/pitch/roll].),
            'hand_image': Image(shape=(224, 224, 3), dtype=uint8, description=Hand camera RGB observation.),
            'image': Image(shape=(224, 224, 3), dtype=uint8, description=Main camera RGB observation.),
            'image2': Image(shape=(224, 224, 3), dtype=uint8, description=Another camera RGB observation from different view point.),
            'joint_state': Tensor(shape=(14,), dtype=float32, description=Robot joint state, consists of [7x robot joint angles, 7x robot joint velocity].),
            'joint_trajectory': Tensor(shape=(21,), dtype=float32, description=Robot joint trajectory, consists of [7x robot joint angles, 7x robot joint velocity, 7x robot joint acceralation].),
        }),
        'reward': Scalar(shape=(), dtype=float32, description=Reward if provided, 1 on final step for demos.),
    }),
})

@misc{matsushima2023weblab,
  title={Weblab xArm Dataset},
  author={Tatsuya Matsushima and Hiroki Furuta and Yusuke Iwasawa and Yutaka Matsuo},
  year={2023},
}