spoc_robot

Membelah Contoh
'train' 212.043
'val' 21.108
  • Struktur fitur :
FeaturesDict({
    'episode_metadata': FeaturesDict({
        'file_path': string,
        'task_target_split': string,
        'task_type': string,
    }),
    'steps': Dataset({
        'action': Tensor(shape=(9,), dtype=float32),
        'discount': Scalar(shape=(), dtype=float32),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'language_instruction': string,
        'observation': FeaturesDict({
            'an_object_is_in_hand': Scalar(shape=(), dtype=bool),
            'house_index': Scalar(shape=(), dtype=int64),
            'hypothetical_task_success': Scalar(shape=(), dtype=bool),
            'image': Image(shape=(224, 384, 3), dtype=uint8),
            'image_manipulation': Image(shape=(224, 384, 3), dtype=uint8),
            'last_action_is_random': Scalar(shape=(), dtype=bool),
            'last_action_str': string,
            'last_action_success': Scalar(shape=(), dtype=bool),
            'last_agent_location': Tensor(shape=(6,), dtype=float32),
            'manip_object_bbox': Tensor(shape=(10,), dtype=float32),
            'minimum_l2_target_distance': Scalar(shape=(), dtype=float32),
            'minimum_visible_target_alignment': Scalar(shape=(), dtype=float32),
            'nav_object_bbox': Tensor(shape=(10,), dtype=float32),
            'relative_arm_location_metadata': Tensor(shape=(4,), dtype=float32),
            'room_current_seen': Scalar(shape=(), dtype=bool),
            'rooms_seen': Scalar(shape=(), dtype=int64),
            'visible_target_4m_count': Scalar(shape=(), dtype=int64),
        }),
        'reward': Scalar(shape=(), dtype=float32),
    }),
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Tipe D Keterangan
FiturDict
episode_metadata FiturDict
episode_metadata/file_path Tensor rangkaian
episode_metadata/task_target_split Tensor rangkaian
episode_metadata/task_type Tensor rangkaian
tangga Kumpulan data
langkah/tindakan Tensor (9,) float32
langkah/diskon Skalar float32
langkah/adalah_pertama Tensor bodoh
langkah/adalah_terakhir Tensor bodoh
langkah/is_terminal Tensor bodoh
langkah/bahasa_instruksi Tensor rangkaian
langkah/pengamatan FiturDict
langkah/pengamatan/an_object_is_in_hand Skalar bodoh
langkah/pengamatan/house_index Skalar int64
langkah/pengamatan/hipotetis_tugas_sukses Skalar bodoh
langkah/pengamatan/gambar Gambar (224, 384, 3) uint8
langkah/pengamatan/manipulasi_gambar Gambar (224, 384, 3) uint8
langkah/pengamatan/last_action_is_random Skalar bodoh
langkah/pengamatan/last_action_str Tensor rangkaian
langkah/pengamatan/last_action_success Skalar bodoh
langkah/pengamatan/last_agent_location Tensor (6,) float32
langkah/pengamatan/manip_object_bbox Tensor (10,) float32
langkah/pengamatan/minimum_l2_target_distance Skalar float32
langkah/pengamatan/minimum_visible_target_alignment Skalar float32
langkah/pengamatan/nav_object_bbox Tensor (10,) float32
langkah/pengamatan/relative_arm_location_metadata Tensor (4,) float32
langkah/observasi/ruangan_saat ini_dilihat Skalar bodoh
langkah/observasi/ruangan_dilihat Skalar int64
langkah/pengamatan/visibel_target_4m_count Skalar int64
langkah/hadiah Skalar float32
  • Kutipan :
@article{spoc2023,
    author    = {Kiana Ehsani, Tanmay Gupta, Rose Hendrix, Jordi Salvador, Luca Weihs, Kuo-Hao Zeng, Kunal Pratap Singh, Yejin Kim, Winson Han, Alvaro Herrasti, Ranjay Krishna, Dustin Schwenk, Eli VanderBilt, Aniruddha Kembhavi},
    title     = {Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World},
    journal   = {arXiv},
    year      = {2023},
    eprint    = {2312.02976},
}