fractal20220817_data

FeaturesDict({
    'aspects': FeaturesDict({
        'already_success': bool,
        'feasible': bool,
        'has_aspects': bool,
        'success': bool,
        'undesirable': bool,
    }),
    'attributes': FeaturesDict({
        'collection_mode': int64,
        'collection_mode_name': string,
        'data_type': int64,
        'data_type_name': string,
        'env': int64,
        'env_name': string,
        'location': int64,
        'location_name': string,
        'objects_family': int64,
        'objects_family_name': string,
        'task_family': int64,
        'task_family_name': string,
    }),
    'steps': Dataset({
        'action': FeaturesDict({
            'base_displacement_vector': Tensor(shape=(2,), dtype=float32),
            'base_displacement_vertical_rotation': Tensor(shape=(1,), dtype=float32),
            'gripper_closedness_action': Tensor(shape=(1,), dtype=float32, description=continuous gripper position),
            'rotation_delta': Tensor(shape=(3,), dtype=float32, description=rpy commanded orientation displacement, in base-relative frame),
            'terminate_episode': Tensor(shape=(3,), dtype=int32),
            'world_vector': Tensor(shape=(3,), dtype=float32, description=commanded end-effector displacement, in base-relative frame),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'base_pose_tool_reached': Tensor(shape=(7,), dtype=float32, description=end-effector base-relative position+quaternion pose),
            'gripper_closed': Tensor(shape=(1,), dtype=float32),
            'gripper_closedness_commanded': Tensor(shape=(1,), dtype=float32, description=continuous gripper position),
            'height_to_bottom': Tensor(shape=(1,), dtype=float32, description=height of end-effector from ground),
            'image': Image(shape=(256, 320, 3), dtype=uint8),
            'natural_language_embedding': Tensor(shape=(512,), dtype=float32),
            'natural_language_instruction': string,
            'orientation_box': Tensor(shape=(2, 3), dtype=float32),
            'orientation_start': Tensor(shape=(4,), dtype=float32),
            'robot_orientation_positions_box': Tensor(shape=(3, 3), dtype=float32),
            'rotation_delta_to_go': Tensor(shape=(3,), dtype=float32, description=rotational displacement from current orientation to target),
            'src_rotation': Tensor(shape=(4,), dtype=float32),
            'vector_to_go': Tensor(shape=(3,), dtype=float32, description=displacement from current end-effector position to target),
            'workspace_bounds': Tensor(shape=(3, 3), dtype=float32),
        }),
        'reward': Scalar(shape=(), dtype=float32),
    }),
})

@article{brohan2022rt,
  title={Rt-1: Robotics transformer for real-world control at scale},
  author={Brohan, Anthony and Brown, Noah and Carbajal, Justice and Chebotar, Yevgen and Dabis, Joseph and Finn, Chelsea and Gopalakrishnan, Keerthana and Hausman, Karol and Herzog, Alex and Hsu, Jasmine and others},
  journal={arXiv preprint arXiv:2212.06817},
  year={2022}
}