asu_table_top_converted_externally_to_rlds

• 説明：

卓上でピック/配置/回転タスクを実行する UR5

• ホームページ: https://link.springer.com/article/10.1007/s10514-023-10129-1

• ソースコード: tfds.robotics.rtx.AsuTableTopConvertedExternallyToRlds

• バージョン:

  • 0.1.0 (デフォルト): 初期リリース。

• ダウンロードサイズ: Unknown size

• データセットのサイズ: 737.60 MiB

• 自動キャッシュ(ドキュメント): いいえ

• 分割:

• 機能の構造:

FeaturesDict({
    'episode_metadata': FeaturesDict({
        'file_path': Text(shape=(), dtype=string),
    }),
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float32, description=Robot action, consists of [7x joint velocities, 2x gripper velocities, 1x terminate episode].),
        'action_delta': Tensor(shape=(7,), dtype=float32, description=Robot delta action, consists of [7x joint velocities, 2x gripper velocities, 1x terminate episode].),
        'action_inst': Text(shape=(), dtype=string),
        'discount': Scalar(shape=(), dtype=float32, description=Discount if provided, default to 1.),
        'goal_object': Text(shape=(), dtype=string),
        'ground_truth_states': FeaturesDict({
            'EE': Tensor(shape=(6,), dtype=float32, description=xyzrpy),
            'bottle': Tensor(shape=(6,), dtype=float32, description=xyzrpy),
            'bread': Tensor(shape=(6,), dtype=float32, description=xyzrpy),
            'coke': Tensor(shape=(6,), dtype=float32, description=xyzrpy),
            'cube': Tensor(shape=(6,), dtype=float32, description=xyzrpy),
            'milk': Tensor(shape=(6,), dtype=float32, description=xyzrpy),
            'pepsi': Tensor(shape=(6,), dtype=float32, description=xyzrpy),
        }),
        '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=(224, 224, 3), dtype=uint8, description=Main camera RGB observation.),
            'state': Tensor(shape=(7,), dtype=float32, description=Robot state, consists of [6x robot joint angles, 1x gripper position].),
            'state_vel': Tensor(shape=(7,), dtype=float32, description=Robot joint velocity, consists of [6x robot joint angles, 1x gripper position].),
        }),
        'reward': Scalar(shape=(), dtype=float32, description=Reward if provided, 1 on final step for demos.),
    }),
})

• 機能ドキュメント:

• 監視キー( as_supervised docを参照): None

• 図( tfds.show_examples ): サポートされていません。

• 例( tfds.as_dataframe ):

• 引用：

@inproceedings{zhou2023modularity,
  title={Modularity through Attention: Efficient Training and Transfer of Language-Conditioned Policies for Robot Manipulation},
  author={Zhou, Yifan and Sonawani, Shubham and Phielipp, Mariano and Stepputtis, Simon and Amor, Heni},
  booktitle={Conference on Robot Learning},
  pages={1684--1695},
  year={2023},
  organization={PMLR}
}
@article{zhou2023learning,
  title={Learning modular language-conditioned robot policies through attention},
  author={Zhou, Yifan and Sonawani, Shubham and Phielipp, Mariano and Ben Amor, Heni and Stepputtis, Simon},
  journal={Autonomous Robots},
  pages={1--21},
  year={2023},
  publisher={Springer}
}