tokyo_u_lsmo_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 endeffector position, 3x euler angles,1x gripper action].),
        '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({
            'image': Image(shape=(120, 120, 3), dtype=uint8, description=Main camera RGB observation.),
            'state': Tensor(shape=(13,), dtype=float32, description=Robot state, consists of [3x endeffector position, 3x euler angles,6x robot joint angles, 1x gripper position].),
        }),
        'reward': Scalar(shape=(), dtype=float32, description=Reward if provided, 1 on final step for demos.),
    }),
})

@Article{Osa22,
  author  = {Takayuki Osa},
  journal = {The International Journal of Robotics Research},
  title   = {Motion Planning by Learning the Solution Manifold in Trajectory Optimization},
  year    = {2022},
  number  = {3},
  pages   = {291--311},
  volume  = {41},
}