asu_table_top_converted_externally_to_rlds

• Keterangan :

UR5 melakukan tugas pengambilan/tempat/putar di atas meja

• Beranda : https://link.springer.com/article/10.1007/s10514-023-10129-1

• Kode sumber : tfds.robotics.rtx.AsuTableTopConvertedExternallyToRlds

• Versi :

  • 0.1.0 (default): Rilis awal.

• Ukuran unduhan : Unknown size

• Ukuran kumpulan data : 737.60 MiB

• Cache otomatis ( dokumentasi ): Tidak

• Perpecahan :

• Struktur fitur :

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),
    }),
})

• Dokumentasi fitur :

• Kunci yang diawasi (Lihat dokumen as_supervised ): None

• Gambar ( tfds.show_examples ): Tidak didukung.

• Contoh ( tfds.as_dataframe ):

• Kutipan :

@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}
}