쿠카

  • 설명 :

빈 피킹 및 재배치 작업

나뉘다
'train' 580,392
  • 기능 구조 :
FeaturesDict({
    '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),
            'rotation_delta': Tensor(shape=(3,), dtype=float32),
            'terminate_episode': Tensor(shape=(3,), dtype=int32),
            'world_vector': Tensor(shape=(3,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'clip_function_input/base_pose_tool_reached': Tensor(shape=(7,), dtype=float32),
            'clip_function_input/workspace_bounds': Tensor(shape=(3, 3), dtype=float32),
            'gripper_closed': Tensor(shape=(1,), dtype=float32),
            'height_to_bottom': Tensor(shape=(1,), dtype=float32),
            'image': Image(shape=(512, 640, 3), dtype=uint8),
            'natural_language_embedding': Tensor(shape=(512,), dtype=float32),
            'natural_language_instruction': string,
            'task_id': Tensor(shape=(1,), dtype=float32),
        }),
        'reward': Scalar(shape=(), dtype=float32),
    }),
    'success': bool,
})
  • 기능 문서 :
특징 수업 모양 Dtype 설명
특징Dict
단계 데이터세트
단계/작업 특징Dict
단계/액션/base_displacement_Vector 텐서 (2,) float32
단계/액션/base_displacement_vertical_rotation 텐서 (1,) float32
단계/작업/gripper_closedness_action 텐서 (1,) float32
단계/작업/회전_델타 텐서 (삼,) float32
단계/작업/종료_에피소드 텐서 (삼,) 정수32
단계/행동/세계_벡터 텐서 (삼,) float32
걸음수/is_first 텐서 부울
걸음수/is_last 텐서 부울
단계/is_terminal 텐서 부울
단계/관찰 특징Dict
단계/관찰/clip_function_input/base_pose_tool_reached 텐서 (7,) float32
단계/관찰/clip_function_input/workspace_bounds 텐서 (3, 3) float32
단계/관찰/gripper_closed 텐서 (1,) float32
단계/관찰/height_to_bottom 텐서 (1,) float32
단계/관찰/이미지 영상 (512, 640, 3) uint8
단계/관찰/natural_언어_임베딩 텐서 (512,) float32
단계/관찰/natural_lang_instruction 텐서
단계/관찰/task_id 텐서 (1,) float32
걸음 수/보상 스칼라 float32
성공 텐서 부울
  • 인용 :
@article{kalashnikov2018qt,
  title={Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation},
  author={Kalashnikov, Dmitry and Irpan, Alex and Pastor, Peter and Ibarz, Julian and Herzog, Alexander and Jang, Eric and Quillen, Deirdre and Holly, Ethan and Kalakrishnan, Mrinal and Vanhoucke, Vincent and others},
  journal={arXiv preprint arXiv:1806.10293},
  year={2018}
}