stanford_mask_vit_converted_externally_to_rlds

  • Keterangan :

Sawyer mendorong dan mengambil benda di tempat sampah

Membelah Contoh
'train' 9.109
'val' 91
  • Struktur fitur :
FeaturesDict({
    'episode_metadata': FeaturesDict({
        'file_path': Text(shape=(), dtype=string),
    }),
    'steps': Dataset({
        'action': Tensor(shape=(5,), dtype=float32, description=Robot action, consists of [3x change in end effector position, 1x gripper yaw, 1x open/close gripper (-1 means to open the gripper, 1 means 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({
            'end_effector_pose': Tensor(shape=(5,), dtype=float32, description=Robot end effector pose, consists of [3x Cartesian position, 1x gripper yaw, 1x gripper position]. This is the state used in the MaskViT paper.),
            'finger_sensors': Tensor(shape=(1,), dtype=float32, description=1x Sawyer gripper finger sensors.),
            'high_bound': Tensor(shape=(5,), dtype=float32, description=High bound for end effector pose normalization. Consists of [3x Cartesian position, 1x gripper yaw, 1x gripper position].),
            'image': Image(shape=(480, 480, 3), dtype=uint8, description=Main camera RGB observation.),
            'low_bound': Tensor(shape=(5,), dtype=float32, description=Low bound for end effector pose normalization. Consists of [3x Cartesian position, 1x gripper yaw, 1x gripper position].),
            'state': Tensor(shape=(15,), dtype=float32, description=Robot state, consists of [7x robot joint angles, 7x robot joint velocities,1x gripper position].),
        }),
        'reward': Scalar(shape=(), dtype=float32, description=Reward if provided, 1 on final step for demos.),
    }),
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Tipe D Keterangan
FiturDict
episode_metadata FiturDict
episode_metadata/file_path Teks rangkaian Jalur ke file data asli.
tangga Kumpulan data
langkah/tindakan Tensor (5,) float32 Aksi robot, terdiri dari [3x perubahan posisi end effector, 1x gripper yaw, 1x buka/tutup gripper (-1 artinya buka gripper, 1 artinya tutup)].
langkah/diskon Skalar float32 Diskon jika disediakan, defaultnya adalah 1.
langkah/adalah_pertama Tensor bodoh
langkah/adalah_terakhir Tensor bodoh
langkah/is_terminal Tensor bodoh
langkah/bahasa_embedding Tensor (512,) float32 Penyematan bahasa Kona. Lihat https://tfhub.dev/google/universal-sentence-encoder-large/5
langkah/bahasa_instruksi Teks rangkaian Instruksi Bahasa.
langkah/pengamatan FiturDict
langkah/pengamatan/end_effector_pose Tensor (5,) float32 Pose efektor ujung robot, terdiri dari [3x posisi Cartesian, 1x gripper yaw, 1x posisi gripper]. Ini adalah keadaan yang digunakan dalam makalah MaskViT.
langkah/pengamatan/finger_sensors Tensor (1,) float32 1x sensor jari gripper Sawyer.
langkah/pengamatan/high_bound Tensor (5,) float32 Batas tinggi untuk normalisasi pose efektor akhir. Terdiri dari [3x posisi Cartesian, 1x gripper yaw, 1x posisi gripper].
langkah/pengamatan/gambar Gambar (480, 480, 3) uint8 Pengamatan RGB kamera utama.
langkah/pengamatan/low_bound Tensor (5,) float32 Batas rendah untuk normalisasi pose efektor akhir. Terdiri dari [3x posisi Cartesian, 1x gripper yaw, 1x posisi gripper].
langkah/pengamatan/keadaan Tensor (15,) float32 Keadaan robot, terdiri dari [7x sudut sambungan robot, 7x kecepatan sambungan robot, 1x posisi gripper].
langkah/hadiah Skalar float32 Hadiah jika diberikan, 1 pada langkah terakhir untuk demo.
@inproceedings{gupta2022maskvit,
  title={MaskViT: Masked Visual Pre-Training for Video Prediction},
  author={Agrim Gupta and Stephen Tian and Yunzhi Zhang and Jiajun Wu and Roberto Martín-Martín and Li Fei-Fei},
  booktitle={International Conference on Learning Representations},
  year={2022}
}