nyu_rot_dataset_converted_externally_to_rlds

• Tanım :

xArm kısa ufuklu masa üstü görevler

• Ana sayfa : https://rot-robot.github.io/

• Kaynak kodu : tfds.robotics.rtx.NyuRotDatasetConvertedExternallyToRlds

• Sürümler :

  • 0.1.0 (varsayılan): İlk sürüm.

• İndirme boyutu : Unknown size

• Veri kümesi boyutu : 5.33 MiB

• Otomatik önbelleğe alınmış ( belgeler ): Evet

• Bölünmeler :

• Özellik yapısı :

FeaturesDict({
    'episode_metadata': FeaturesDict({
        'file_path': Text(shape=(), dtype=string),
    }),
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float32, description=Robot action, consists of [3x robot end effector delta positions, 3x robot end effector rotations (roll, pitch, yaw),1x gripper open/close (0-open, 1-closed)].),
        '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=(84, 84, 3), dtype=uint8, description=Main camera RGB observation.),
            'state': Tensor(shape=(7,), dtype=float32, description=Robot state, consists of [3x robot end effector positions, 3x robot end effector rotations (roll, pitch, yaw),1x gripper open/close (0-open, 1-closed)].),
        }),
        'reward': Scalar(shape=(), dtype=float32, description=Reward if provided, 1 on final step for demos.),
    }),
})

• Özellik belgeleri :

• Denetlenen anahtarlar ( as_supervised belgesine bakın): None

• Şekil ( tfds.show_examples ): Desteklenmiyor.

• Örnekler ( tfds.as_dataframe ):

• Alıntı :

@inproceedings{haldar2023watch,
  title={Watch and match: Supercharging imitation with regularized optimal transport},
  author={Haldar, Siddhant and Mathur, Vaibhav and Yarats, Denis and Pinto, Lerrel},
  booktitle={Conference on Robot Learning},
  pages={32--43},
  year={2023},
  organization={PMLR}
}