asu_table_top_converted_externally_to_rlds

• Descripción :

UR5 realiza tareas de selección/colocación/rotación de mesa

• Página de inicio : https://link.springer.com/article/10.1007/s10514-023-10129-1

• Código fuente : tfds.robotics.rtx.AsuTableTopConvertedExternallyToRlds

• Versiones :

  • 0.1.0 (predeterminado): versión inicial.

• Tamaño de descarga : Unknown size

• Tamaño del conjunto de datos : 737.60 MiB

• Almacenamiento en caché automático ( documentación ): No

• Divisiones :

• Estructura de características :

FeaturesDict({
    'episode_metadata': FeaturesDict({
        'file_path': Text(shape=(), dtype=string),
    }),
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float32),
        'action_delta': Tensor(shape=(7,), dtype=float32),
        'action_inst': Text(shape=(), dtype=string),
        'discount': Scalar(shape=(), dtype=float32),
        'goal_object': Text(shape=(), dtype=string),
        'ground_truth_states': FeaturesDict({
            'EE': Tensor(shape=(6,), dtype=float32),
            'bottle': Tensor(shape=(6,), dtype=float32),
            'bread': Tensor(shape=(6,), dtype=float32),
            'coke': Tensor(shape=(6,), dtype=float32),
            'cube': Tensor(shape=(6,), dtype=float32),
            'milk': Tensor(shape=(6,), dtype=float32),
            'pepsi': Tensor(shape=(6,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'language_embedding': Tensor(shape=(512,), dtype=float32),
        'language_instruction': Text(shape=(), dtype=string),
        'observation': FeaturesDict({
            'image': Image(shape=(224, 224, 3), dtype=uint8),
            'state': Tensor(shape=(7,), dtype=float32),
            'state_vel': Tensor(shape=(7,), dtype=float32),
        }),
        'reward': Scalar(shape=(), dtype=float32),
    }),
})

• Documentación de funciones :

• Claves supervisadas (ver documento as_supervised ): None

• Figura ( tfds.show_examples ): no compatible.

• Ejemplos ( tfds.as_dataframe ):

• Cita :

@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}
}