usc_cloth_sim_converted_externally_to_rlds

• Descrizione :

Compiti di interazione con i tessuti di Franka

• Pagina iniziale : https://uscresl.github.io/dmfd/

• Codice sorgente : tfds.robotics.rtx.UscClothSimConvertedExternallyToRlds

• Versioni :

  • 0.1.0 (predefinito): versione iniziale.

• Dimensioni del download : Unknown size

• Dimensione del set di dati : 254.52 MiB

• Memorizzazione nella cache automatica ( documentazione ): No

• Divide :

• Struttura delle caratteristiche :

FeaturesDict({
    'episode_metadata': FeaturesDict({
        'file_path': Text(shape=(), dtype=string),
    }),
    'steps': Dataset({
        'action': Tensor(shape=(4,), dtype=float32, description=Robot action, consists of x,y,z goal and picker commandpicker<0.5 = open, picker>0.5 = close.),
        '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=(32, 32, 3), dtype=uint8, description=Image observation of cloth.),
        }),
        'reward': Scalar(shape=(), dtype=float32, description=Reward as a normalized performance metric in [0, 1].0 = no change from initial state. 1 = perfect fold.-ve performance means the cloth is worse off than initial state.),
    }),
})

• Documentazione delle funzionalità :

• Chiavi supervisionate (vedi il documento as_supervised ): None

• Figura ( tfds.show_examples ): non supportato.

• Esempi ( tfds.as_dataframe ): Mancante.

• Citazione :

@article{salhotra2022dmfd,
    author={Salhotra, Gautam and Liu, I-Chun Arthur and Dominguez-Kuhne, Marcus and Sukhatme, Gaurav S.},
    journal={IEEE Robotics and Automation Letters},
    title={Learning Deformable Object Manipulation From Expert Demonstrations},
    year={2022},
    volume={7},
    number={4},
    pages={8775-8782},
    doi={10.1109/LRA.2022.3187843}
}