fraktal20220817_data

  • Keterangan :

Manipulasi permukaan meja dengan 17 objek

Membelah Contoh
'train' 87.212
  • Struktur fitur :
FeaturesDict({
    'aspects': FeaturesDict({
        'already_success': bool,
        'feasible': bool,
        'has_aspects': bool,
        'success': bool,
        'undesirable': bool,
    }),
    'attributes': FeaturesDict({
        'collection_mode': int64,
        'collection_mode_name': string,
        'data_type': int64,
        'data_type_name': string,
        'env': int64,
        'env_name': string,
        'location': int64,
        'location_name': string,
        'objects_family': int64,
        'objects_family_name': string,
        'task_family': int64,
        'task_family_name': string,
    }),
    'steps': Dataset({
        'action': FeaturesDict({
            'base_displacement_vector': Tensor(shape=(2,), dtype=float32),
            'base_displacement_vertical_rotation': Tensor(shape=(1,), dtype=float32),
            'gripper_closedness_action': Tensor(shape=(1,), dtype=float32, description=continuous gripper position),
            'rotation_delta': Tensor(shape=(3,), dtype=float32, description=rpy commanded orientation displacement, in base-relative frame),
            'terminate_episode': Tensor(shape=(3,), dtype=int32),
            'world_vector': Tensor(shape=(3,), dtype=float32, description=commanded end-effector displacement, in base-relative frame),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'base_pose_tool_reached': Tensor(shape=(7,), dtype=float32, description=end-effector base-relative position+quaternion pose),
            'gripper_closed': Tensor(shape=(1,), dtype=float32),
            'gripper_closedness_commanded': Tensor(shape=(1,), dtype=float32, description=continuous gripper position),
            'height_to_bottom': Tensor(shape=(1,), dtype=float32, description=height of end-effector from ground),
            'image': Image(shape=(256, 320, 3), dtype=uint8),
            'natural_language_embedding': Tensor(shape=(512,), dtype=float32),
            'natural_language_instruction': string,
            'orientation_box': Tensor(shape=(2, 3), dtype=float32),
            'orientation_start': Tensor(shape=(4,), dtype=float32),
            'robot_orientation_positions_box': Tensor(shape=(3, 3), dtype=float32),
            'rotation_delta_to_go': Tensor(shape=(3,), dtype=float32, description=rotational displacement from current orientation to target),
            'src_rotation': Tensor(shape=(4,), dtype=float32),
            'vector_to_go': Tensor(shape=(3,), dtype=float32, description=displacement from current end-effector position to target),
            'workspace_bounds': Tensor(shape=(3, 3), dtype=float32),
        }),
        'reward': Scalar(shape=(), dtype=float32),
    }),
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Tipe D Keterangan
FiturDict
aspek FiturDict Aspek Sesi untuk peringkat crowdcompute
aspek/sudah_sukses Tensor bodoh
aspek/layak Tensor bodoh
aspek/has_aspects Tensor bodoh
aspek/keberhasilan Tensor bodoh
aspek/tidak diinginkan Tensor bodoh
atribut FiturDict
atribut/mode_koleksi Tensor int64
atribut/nama_mode_koleksi Tensor rangkaian
atribut/tipe_data Tensor int64
atribut/data_type_name Tensor rangkaian
atribut/env Tensor int64
atribut/env_name Tensor rangkaian
atribut/lokasi Tensor int64
atribut/nama_lokasi Tensor rangkaian
atribut/objek_keluarga Tensor int64
atribut/objek_nama_keluarga Tensor rangkaian
atribut/task_family Tensor int64
atribut/tugas_nama_keluarga Tensor rangkaian
tangga Kumpulan data
langkah/tindakan FiturDict
langkah/tindakan/base_displacement_vector Tensor (2,) float32
langkah/tindakan/base_displacement_vertical_rotation Tensor (1,) float32
langkah/tindakan/gripper_closedness_action Tensor (1,) float32 posisi gripper terus menerus
langkah/tindakan/rotasi_delta Tensor (3,) float32 rpy memerintahkan perpindahan orientasi, dalam kerangka relatif dasar
langkah/tindakan/terminate_episode Tensor (3,) int32
langkah/tindakan/world_vector Tensor (3,) float32 memerintahkan perpindahan efektor akhir, dalam kerangka relatif dasar
langkah/adalah_pertama Tensor bodoh
langkah/adalah_terakhir Tensor bodoh
langkah/is_terminal Tensor bodoh
langkah/pengamatan FiturDict
langkah/pengamatan/base_pose_tool_reached Tensor (7,) float32 posisi relatif dasar efektor akhir + pose angka empat
langkah/pengamatan/gripper_closed Tensor (1,) float32
langkah/pengamatan/gripper_closedness_commanded Tensor (1,) float32 posisi gripper terus menerus
langkah/pengamatan/tinggi_ke_bawah Tensor (1,) float32 ketinggian end-effector dari tanah
langkah/pengamatan/gambar Gambar (256, 320, 3) uint8
langkah/pengamatan/natural_bahasa_embedding Tensor (512,) float32
langkah/pengamatan/instruksi_bahasa_alami Tensor rangkaian
langkah/pengamatan/orientation_box Tensor (2, 3) float32
langkah/pengamatan/orientasi_mulai Tensor (4,) float32
langkah/pengamatan/robot_orientation_positions_box Tensor (3, 3) float32
langkah/pengamatan/rotasi_delta_to_go Tensor (3,) float32 perpindahan rotasi dari orientasi saat ini ke target
langkah/pengamatan/src_rotation Tensor (4,) float32
langkah/pengamatan/vector_to_go Tensor (3,) float32 perpindahan dari posisi efektor akhir saat ini ke target
langkah/pengamatan/ruang kerja_batas Tensor (3, 3) float32
langkah/hadiah Skalar float32
  • Kutipan :
@article{brohan2022rt,
  title={Rt-1: Robotics transformer for real-world control at scale},
  author={Brohan, Anthony and Brown, Noah and Carbajal, Justice and Chebotar, Yevgen and Dabis, Joseph and Finn, Chelsea and Gopalakrishnan, Keerthana and Hausman, Karol and Herzog, Alex and Hsu, Jasmine and others},
  journal={arXiv preprint arXiv:2212.06817},
  year={2022}
}