robomimic_mh

  • Deskripsi :

Kumpulan data manusia campuran Robomimic dikumpulkan oleh beberapa operator kemampuan campuran menggunakan platform RoboTurk . Setiap dataset terdiri dari 200 demonstrasi.

Setiap tugas memiliki dua versi: satu dengan pengamatan dimensi rendah ( low_dim ), dan satu lagi dengan gambar ( image ).

Kumpulan data mengikuti format RLDS untuk mewakili langkah dan episode.

Membelah Contoh
'train' 300
@inproceedings{robomimic2021,
  title={What Matters in Learning from Offline Human Demonstrations for Robot Manipulation},
  author={Ajay Mandlekar and Danfei Xu and Josiah Wong and Soroush Nasiriany
          and Chen Wang and Rohun Kulkarni and Li Fei-Fei and Silvio Savarese
          and Yuke Zhu and Roberto Mart\'{i}n-Mart\'{i}n},
  booktitle={Conference on Robot Learning},
  year={2021}
}

robomimic_mh/lift_mh_image (konfigurasi default)

  • Ukuran unduhan : 2.50 GiB

  • Ukuran dataset : 363.18 MiB

  • Di-cache otomatis ( dokumentasi ): Tidak

  • Struktur fitur :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_operator_1': bool,
    'better_operator_1_train': bool,
    'better_operator_1_valid': bool,
    'better_operator_2': bool,
    'better_operator_2_train': bool,
    'better_operator_2_valid': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_operator_1': bool,
    'okay_operator_1_train': bool,
    'okay_operator_1_valid': bool,
    'okay_operator_2': bool,
    'okay_operator_2_train': bool,
    'okay_operator_2_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'agentview_image': Image(shape=(84, 84, 3), dtype=uint8),
            'object': Tensor(shape=(10,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
        }),
        'reward': float64,
        'states': Tensor(shape=(32,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_operator_1': bool,
    'worse_operator_1_train': bool,
    'worse_operator_1_valid': bool,
    'worse_operator_2': bool,
    'worse_operator_2_train': bool,
    'worse_operator_2_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Dtype Keterangan
fiturDict
20_persen Tensor bool
20_persen_kereta Tensor bool
20_persen_valid Tensor bool
50 persen Tensor bool
50_persen_kereta Tensor bool
50_persen_valid Tensor bool
lebih baik Tensor bool
better_operator_1 Tensor bool
better_operator_1_train Tensor bool
better_operator_1_valid Tensor bool
better_operator_2 Tensor bool
better_operator_2_train Tensor bool
better_operator_2_valid Tensor bool
kereta_lebih baik Tensor bool
lebih baik_valid Tensor bool
episode_id Tensor rangkaian
cakrawala Tensor int32
Oke Tensor bool
oke_lebih baik Tensor bool
oke_better_train Tensor bool
oke_better_valid Tensor bool
oke_operator_1 Tensor bool
oke_operator_1_kereta Tensor bool
oke_operator_1_valid Tensor bool
oke_operator_2 Tensor bool
oke_operator_2_kereta Tensor bool
oke_operator_2_valid Tensor bool
oke_kereta Tensor bool
oke_valid Tensor bool
Langkah Himpunan data
langkah/tindakan Tensor (7,) float64
langkah/diskon Tensor int32
langkah/adalah_pertama Tensor bool
langkah/is_last Tensor bool
langkah/is_terminal Tensor bool
langkah/pengamatan fiturDict
langkah/pengamatan/agentview_image Gambar (84, 84, 3) uint8
langkah/pengamatan/objek Tensor (10,) float64
langkah/pengamatan/robot0_eef_pos Tensor (3,) float64 Posisi efektor akhir
langkah/pengamatan/robot0_eef_quat Tensor (4,) float64 Orientasi efektor akhir
langkah/pengamatan/robot0_eef_vel_ang Tensor (3,) float64 Kecepatan sudut end-effector
langkah/pengamatan/robot0_eef_vel_lin Tensor (3,) float64 kecepatan kartesius akhir-efektor
langkah/pengamatan/robot0_eye_in_hand_image Gambar (84, 84, 3) uint8
langkah/pengamatan/robot0_gripper_qpos Tensor (2,) float64 Posisi gripper
langkah/pengamatan/robot0_gripper_qvel Tensor (2,) float64 Kecepatan gripper
langkah/pengamatan/robot0_joint_pos Tensor (7,) float64 7DOF posisi bersama
langkah/pengamatan/robot0_joint_pos_cos Tensor (7,) float64
langkah/pengamatan/robot0_joint_pos_sin Tensor (7,) float64
langkah/pengamatan/robot0_joint_vel Tensor (7,) float64 kecepatan sambungan 7DOF
langkah/hadiah Tensor float64
langkah/keadaan Tensor (32,) float64
kereta Tensor bool
sah Tensor bool
lebih buruk Tensor bool
lebih buruk_lebih baik Tensor bool
lebih buruk_better_train Tensor bool
lebih buruk_lebih baik_valid Tensor bool
lebih buruk_oke Tensor bool
lebih buruk_oke_kereta Tensor bool
lebih buruk_oke_valid Tensor bool
bad_operator_1 Tensor bool
bad_operator_1_train Tensor bool
bad_operator_1_valid Tensor bool
bad_operator_2 Tensor bool
bad_operator_2_train Tensor bool
bad_operator_2_valid Tensor bool
kereta_buruk Tensor bool
lebih buruk_valid Tensor bool

robomimic_mh/lift_mh_low_dim

  • Ukuran unduhan : 45.73 MiB

  • Ukuran dataset : 27.26 MiB

  • Di-cache otomatis ( dokumentasi ): Ya

  • Struktur fitur :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_operator_1': bool,
    'better_operator_1_train': bool,
    'better_operator_1_valid': bool,
    'better_operator_2': bool,
    'better_operator_2_train': bool,
    'better_operator_2_valid': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_operator_1': bool,
    'okay_operator_1_train': bool,
    'okay_operator_1_valid': bool,
    'okay_operator_2': bool,
    'okay_operator_2_train': bool,
    'okay_operator_2_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'object': Tensor(shape=(10,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
        }),
        'reward': float64,
        'states': Tensor(shape=(32,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_operator_1': bool,
    'worse_operator_1_train': bool,
    'worse_operator_1_valid': bool,
    'worse_operator_2': bool,
    'worse_operator_2_train': bool,
    'worse_operator_2_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Dtype Keterangan
fiturDict
20_persen Tensor bool
20_persen_kereta Tensor bool
20_persen_valid Tensor bool
50 persen Tensor bool
50_persen_kereta Tensor bool
50_persen_valid Tensor bool
lebih baik Tensor bool
better_operator_1 Tensor bool
better_operator_1_train Tensor bool
better_operator_1_valid Tensor bool
better_operator_2 Tensor bool
better_operator_2_train Tensor bool
better_operator_2_valid Tensor bool
kereta_lebih baik Tensor bool
lebih baik_valid Tensor bool
episode_id Tensor rangkaian
cakrawala Tensor int32
Oke Tensor bool
oke_lebih baik Tensor bool
oke_better_train Tensor bool
oke_better_valid Tensor bool
oke_operator_1 Tensor bool
oke_operator_1_kereta Tensor bool
oke_operator_1_valid Tensor bool
oke_operator_2 Tensor bool
oke_operator_2_kereta Tensor bool
oke_operator_2_valid Tensor bool
oke_kereta Tensor bool
oke_valid Tensor bool
Langkah Himpunan data
langkah/tindakan Tensor (7,) float64
langkah/diskon Tensor int32
langkah/adalah_pertama Tensor bool
langkah/is_last Tensor bool
langkah/is_terminal Tensor bool
langkah/pengamatan fiturDict
langkah/pengamatan/objek Tensor (10,) float64
langkah/pengamatan/robot0_eef_pos Tensor (3,) float64 Posisi efektor akhir
langkah/pengamatan/robot0_eef_quat Tensor (4,) float64 Orientasi efektor akhir
langkah/pengamatan/robot0_eef_vel_ang Tensor (3,) float64 Kecepatan sudut end-effector
langkah/pengamatan/robot0_eef_vel_lin Tensor (3,) float64 kecepatan kartesius akhir-efektor
langkah/pengamatan/robot0_gripper_qpos Tensor (2,) float64 Posisi gripper
langkah/pengamatan/robot0_gripper_qvel Tensor (2,) float64 Kecepatan gripper
langkah/pengamatan/robot0_joint_pos Tensor (7,) float64 7DOF posisi bersama
langkah/pengamatan/robot0_joint_pos_cos Tensor (7,) float64
langkah/pengamatan/robot0_joint_pos_sin Tensor (7,) float64
langkah/pengamatan/robot0_joint_vel Tensor (7,) float64 kecepatan sambungan 7DOF
langkah/hadiah Tensor float64
langkah/keadaan Tensor (32,) float64
kereta Tensor bool
sah Tensor bool
lebih buruk Tensor bool
lebih buruk_lebih baik Tensor bool
lebih buruk_better_train Tensor bool
lebih buruk_lebih baik_valid Tensor bool
lebih buruk_oke Tensor bool
lebih buruk_oke_kereta Tensor bool
lebih buruk_oke_valid Tensor bool
bad_operator_1 Tensor bool
bad_operator_1_train Tensor bool
bad_operator_1_valid Tensor bool
bad_operator_2 Tensor bool
bad_operator_2_train Tensor bool
bad_operator_2_valid Tensor bool
kereta_buruk Tensor bool
lebih buruk_valid Tensor bool

robomimic_mh/can_mh_image

  • Ukuran unduhan : 5.05 GiB

  • Ukuran dataset : 1.23 GiB

  • Di-cache otomatis ( dokumentasi ): Tidak

  • Struktur fitur :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_operator_1': bool,
    'better_operator_1_train': bool,
    'better_operator_1_valid': bool,
    'better_operator_2': bool,
    'better_operator_2_train': bool,
    'better_operator_2_valid': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_operator_1': bool,
    'okay_operator_1_train': bool,
    'okay_operator_1_valid': bool,
    'okay_operator_2': bool,
    'okay_operator_2_train': bool,
    'okay_operator_2_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'agentview_image': Image(shape=(84, 84, 3), dtype=uint8),
            'object': Tensor(shape=(14,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
        }),
        'reward': float64,
        'states': Tensor(shape=(71,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_operator_1': bool,
    'worse_operator_1_train': bool,
    'worse_operator_1_valid': bool,
    'worse_operator_2': bool,
    'worse_operator_2_train': bool,
    'worse_operator_2_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Dtype Keterangan
fiturDict
20_persen Tensor bool
20_persen_kereta Tensor bool
20_persen_valid Tensor bool
50 persen Tensor bool
50_persen_kereta Tensor bool
50_persen_valid Tensor bool
lebih baik Tensor bool
better_operator_1 Tensor bool
better_operator_1_train Tensor bool
better_operator_1_valid Tensor bool
better_operator_2 Tensor bool
better_operator_2_train Tensor bool
better_operator_2_valid Tensor bool
kereta_lebih baik Tensor bool
lebih baik_valid Tensor bool
episode_id Tensor rangkaian
cakrawala Tensor int32
Oke Tensor bool
oke_lebih baik Tensor bool
oke_better_train Tensor bool
oke_better_valid Tensor bool
oke_operator_1 Tensor bool
oke_operator_1_kereta Tensor bool
oke_operator_1_valid Tensor bool
oke_operator_2 Tensor bool
oke_operator_2_kereta Tensor bool
oke_operator_2_valid Tensor bool
oke_kereta Tensor bool
oke_valid Tensor bool
Langkah Himpunan data
langkah/tindakan Tensor (7,) float64
langkah/diskon Tensor int32
langkah/adalah_pertama Tensor bool
langkah/is_last Tensor bool
langkah/is_terminal Tensor bool
langkah/pengamatan fiturDict
langkah/pengamatan/agentview_image Gambar (84, 84, 3) uint8
langkah/pengamatan/objek Tensor (14,) float64
langkah/pengamatan/robot0_eef_pos Tensor (3,) float64 Posisi efektor akhir
langkah/pengamatan/robot0_eef_quat Tensor (4,) float64 Orientasi efektor akhir
langkah/pengamatan/robot0_eef_vel_ang Tensor (3,) float64 Kecepatan sudut end-effector
langkah/pengamatan/robot0_eef_vel_lin Tensor (3,) float64 kecepatan kartesius akhir-efektor
langkah/pengamatan/robot0_eye_in_hand_image Gambar (84, 84, 3) uint8
langkah/pengamatan/robot0_gripper_qpos Tensor (2,) float64 Posisi gripper
langkah/pengamatan/robot0_gripper_qvel Tensor (2,) float64 Kecepatan gripper
langkah/pengamatan/robot0_joint_pos Tensor (7,) float64 7DOF posisi bersama
langkah/pengamatan/robot0_joint_pos_cos Tensor (7,) float64
langkah/pengamatan/robot0_joint_pos_sin Tensor (7,) float64
langkah/pengamatan/robot0_joint_vel Tensor (7,) float64 kecepatan sambungan 7DOF
langkah/hadiah Tensor float64
langkah/keadaan Tensor (71,) float64
kereta Tensor bool
sah Tensor bool
lebih buruk Tensor bool
lebih buruk_lebih baik Tensor bool
lebih buruk_better_train Tensor bool
lebih buruk_lebih baik_valid Tensor bool
lebih buruk_oke Tensor bool
lebih buruk_oke_kereta Tensor bool
lebih buruk_oke_valid Tensor bool
bad_operator_1 Tensor bool
bad_operator_1_train Tensor bool
bad_operator_1_valid Tensor bool
bad_operator_2 Tensor bool
bad_operator_2_train Tensor bool
bad_operator_2_valid Tensor bool
kereta_buruk Tensor bool
lebih buruk_valid Tensor bool

robomimic_mh/can_mh_low_dim

  • Ukuran unduhan : 107.28 MiB

  • Ukuran dataset : 75.19 MiB

  • Di-cache otomatis ( dokumentasi ): Ya

  • Struktur fitur :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_operator_1': bool,
    'better_operator_1_train': bool,
    'better_operator_1_valid': bool,
    'better_operator_2': bool,
    'better_operator_2_train': bool,
    'better_operator_2_valid': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_operator_1': bool,
    'okay_operator_1_train': bool,
    'okay_operator_1_valid': bool,
    'okay_operator_2': bool,
    'okay_operator_2_train': bool,
    'okay_operator_2_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'object': Tensor(shape=(14,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
        }),
        'reward': float64,
        'states': Tensor(shape=(71,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_operator_1': bool,
    'worse_operator_1_train': bool,
    'worse_operator_1_valid': bool,
    'worse_operator_2': bool,
    'worse_operator_2_train': bool,
    'worse_operator_2_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Dtype Keterangan
fiturDict
20_persen Tensor bool
20_persen_kereta Tensor bool
20_persen_valid Tensor bool
50 persen Tensor bool
50_persen_kereta Tensor bool
50_persen_valid Tensor bool
lebih baik Tensor bool
better_operator_1 Tensor bool
better_operator_1_train Tensor bool
better_operator_1_valid Tensor bool
better_operator_2 Tensor bool
better_operator_2_train Tensor bool
better_operator_2_valid Tensor bool
kereta_lebih baik Tensor bool
lebih baik_valid Tensor bool
episode_id Tensor rangkaian
cakrawala Tensor int32
Oke Tensor bool
oke_lebih baik Tensor bool
oke_better_train Tensor bool
oke_better_valid Tensor bool
oke_operator_1 Tensor bool
oke_operator_1_kereta Tensor bool
oke_operator_1_valid Tensor bool
oke_operator_2 Tensor bool
oke_operator_2_kereta Tensor bool
oke_operator_2_valid Tensor bool
oke_kereta Tensor bool
oke_valid Tensor bool
Langkah Himpunan data
langkah/tindakan Tensor (7,) float64
langkah/diskon Tensor int32
langkah/adalah_pertama Tensor bool
langkah/is_last Tensor bool
langkah/is_terminal Tensor bool
langkah/pengamatan fiturDict
langkah/pengamatan/objek Tensor (14,) float64
langkah/pengamatan/robot0_eef_pos Tensor (3,) float64 Posisi efektor akhir
langkah/pengamatan/robot0_eef_quat Tensor (4,) float64 Orientasi efektor akhir
langkah/pengamatan/robot0_eef_vel_ang Tensor (3,) float64 Kecepatan sudut end-effector
langkah/pengamatan/robot0_eef_vel_lin Tensor (3,) float64 kecepatan kartesius akhir-efektor
langkah/pengamatan/robot0_gripper_qpos Tensor (2,) float64 Posisi gripper
langkah/pengamatan/robot0_gripper_qvel Tensor (2,) float64 Kecepatan gripper
langkah/pengamatan/robot0_joint_pos Tensor (7,) float64 7DOF posisi bersama
langkah/pengamatan/robot0_joint_pos_cos Tensor (7,) float64
langkah/pengamatan/robot0_joint_pos_sin Tensor (7,) float64
langkah/pengamatan/robot0_joint_vel Tensor (7,) float64 kecepatan sambungan 7DOF
langkah/hadiah Tensor float64
langkah/keadaan Tensor (71,) float64
kereta Tensor bool
sah Tensor bool
lebih buruk Tensor bool
lebih buruk_lebih baik Tensor bool
lebih buruk_better_train Tensor bool
lebih buruk_lebih baik_valid Tensor bool
lebih buruk_oke Tensor bool
lebih buruk_oke_kereta Tensor bool
lebih buruk_oke_valid Tensor bool
bad_operator_1 Tensor bool
bad_operator_1_train Tensor bool
bad_operator_1_valid Tensor bool
bad_operator_2 Tensor bool
bad_operator_2_train Tensor bool
bad_operator_2_valid Tensor bool
kereta_buruk Tensor bool
lebih buruk_valid Tensor bool

robomimic_mh/square_mh_image

  • Ukuran unduhan : 6.48 GiB

  • Ukuran dataset : 1.07 GiB

  • Di-cache otomatis ( dokumentasi ): Tidak

  • Struktur fitur :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_operator_1': bool,
    'better_operator_1_train': bool,
    'better_operator_1_valid': bool,
    'better_operator_2': bool,
    'better_operator_2_train': bool,
    'better_operator_2_valid': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_operator_1': bool,
    'okay_operator_1_train': bool,
    'okay_operator_1_valid': bool,
    'okay_operator_2': bool,
    'okay_operator_2_train': bool,
    'okay_operator_2_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'agentview_image': Image(shape=(84, 84, 3), dtype=uint8),
            'object': Tensor(shape=(14,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
        }),
        'reward': float64,
        'states': Tensor(shape=(45,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_operator_1': bool,
    'worse_operator_1_train': bool,
    'worse_operator_1_valid': bool,
    'worse_operator_2': bool,
    'worse_operator_2_train': bool,
    'worse_operator_2_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Dtype Keterangan
fiturDict
20_persen Tensor bool
20_persen_kereta Tensor bool
20_persen_valid Tensor bool
50 persen Tensor bool
50_persen_kereta Tensor bool
50_persen_valid Tensor bool
lebih baik Tensor bool
better_operator_1 Tensor bool
better_operator_1_train Tensor bool
better_operator_1_valid Tensor bool
better_operator_2 Tensor bool
better_operator_2_train Tensor bool
better_operator_2_valid Tensor bool
kereta_lebih baik Tensor bool
lebih baik_valid Tensor bool
episode_id Tensor rangkaian
cakrawala Tensor int32
Oke Tensor bool
oke_lebih baik Tensor bool
oke_better_train Tensor bool
oke_better_valid Tensor bool
oke_operator_1 Tensor bool
oke_operator_1_kereta Tensor bool
oke_operator_1_valid Tensor bool
oke_operator_2 Tensor bool
oke_operator_2_kereta Tensor bool
oke_operator_2_valid Tensor bool
oke_kereta Tensor bool
oke_valid Tensor bool
Langkah Himpunan data
langkah/tindakan Tensor (7,) float64
langkah/diskon Tensor int32
langkah/adalah_pertama Tensor bool
langkah/is_last Tensor bool
langkah/is_terminal Tensor bool
langkah/pengamatan fiturDict
langkah/pengamatan/agentview_image Gambar (84, 84, 3) uint8
langkah/pengamatan/objek Tensor (14,) float64
langkah/pengamatan/robot0_eef_pos Tensor (3,) float64 Posisi efektor akhir
langkah/pengamatan/robot0_eef_quat Tensor (4,) float64 Orientasi efektor akhir
langkah/pengamatan/robot0_eef_vel_ang Tensor (3,) float64 Kecepatan sudut end-effector
langkah/pengamatan/robot0_eef_vel_lin Tensor (3,) float64 kecepatan kartesius akhir-efektor
langkah/pengamatan/robot0_eye_in_hand_image Gambar (84, 84, 3) uint8
langkah/pengamatan/robot0_gripper_qpos Tensor (2,) float64 Posisi gripper
langkah/pengamatan/robot0_gripper_qvel Tensor (2,) float64 Kecepatan gripper
langkah/pengamatan/robot0_joint_pos Tensor (7,) float64 7DOF posisi bersama
langkah/pengamatan/robot0_joint_pos_cos Tensor (7,) float64
langkah/pengamatan/robot0_joint_pos_sin Tensor (7,) float64
langkah/pengamatan/robot0_joint_vel Tensor (7,) float64 kecepatan sambungan 7DOF
langkah/hadiah Tensor float64
langkah/keadaan Tensor (45,) float64
kereta Tensor bool
sah Tensor bool
lebih buruk Tensor bool
lebih buruk_lebih baik Tensor bool
lebih buruk_better_train Tensor bool
lebih buruk_lebih baik_valid Tensor bool
lebih buruk_oke Tensor bool
lebih buruk_oke_kereta Tensor bool
lebih buruk_oke_valid Tensor bool
bad_operator_1 Tensor bool
bad_operator_1_train Tensor bool
bad_operator_1_valid Tensor bool
bad_operator_2 Tensor bool
bad_operator_2_train Tensor bool
bad_operator_2_valid Tensor bool
kereta_buruk Tensor bool
lebih buruk_valid Tensor bool

robomimic_mh/square_mh_low_dim

  • Ukuran unduhan : 118.13 MiB

  • Ukuran dataset : 80.37 MiB

  • Di-cache otomatis ( dokumentasi ): Ya

  • Struktur fitur :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_operator_1': bool,
    'better_operator_1_train': bool,
    'better_operator_1_valid': bool,
    'better_operator_2': bool,
    'better_operator_2_train': bool,
    'better_operator_2_valid': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_operator_1': bool,
    'okay_operator_1_train': bool,
    'okay_operator_1_valid': bool,
    'okay_operator_2': bool,
    'okay_operator_2_train': bool,
    'okay_operator_2_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'object': Tensor(shape=(14,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
        }),
        'reward': float64,
        'states': Tensor(shape=(45,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_operator_1': bool,
    'worse_operator_1_train': bool,
    'worse_operator_1_valid': bool,
    'worse_operator_2': bool,
    'worse_operator_2_train': bool,
    'worse_operator_2_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Dtype Keterangan
fiturDict
20_persen Tensor bool
20_persen_kereta Tensor bool
20_persen_valid Tensor bool
50 persen Tensor bool
50_persen_kereta Tensor bool
50_persen_valid Tensor bool
lebih baik Tensor bool
better_operator_1 Tensor bool
better_operator_1_train Tensor bool
better_operator_1_valid Tensor bool
better_operator_2 Tensor bool
better_operator_2_train Tensor bool
better_operator_2_valid Tensor bool
kereta_lebih baik Tensor bool
lebih baik_valid Tensor bool
episode_id Tensor rangkaian
cakrawala Tensor int32
Oke Tensor bool
oke_lebih baik Tensor bool
oke_better_train Tensor bool
oke_better_valid Tensor bool
oke_operator_1 Tensor bool
oke_operator_1_kereta Tensor bool
oke_operator_1_valid Tensor bool
oke_operator_2 Tensor bool
oke_operator_2_kereta Tensor bool
oke_operator_2_valid Tensor bool
oke_kereta Tensor bool
oke_valid Tensor bool
Langkah Himpunan data
langkah/tindakan Tensor (7,) float64
langkah/diskon Tensor int32
langkah/adalah_pertama Tensor bool
langkah/is_last Tensor bool
langkah/is_terminal Tensor bool
langkah/pengamatan fiturDict
langkah/pengamatan/objek Tensor (14,) float64
langkah/pengamatan/robot0_eef_pos Tensor (3,) float64 Posisi efektor akhir
langkah/pengamatan/robot0_eef_quat Tensor (4,) float64 Orientasi efektor akhir
langkah/pengamatan/robot0_eef_vel_ang Tensor (3,) float64 Kecepatan sudut end-effector
langkah/pengamatan/robot0_eef_vel_lin Tensor (3,) float64 kecepatan kartesius akhir-efektor
langkah/pengamatan/robot0_gripper_qpos Tensor (2,) float64 Posisi gripper
langkah/pengamatan/robot0_gripper_qvel Tensor (2,) float64 Kecepatan gripper
langkah/pengamatan/robot0_joint_pos Tensor (7,) float64 7DOF posisi bersama
langkah/pengamatan/robot0_joint_pos_cos Tensor (7,) float64
langkah/pengamatan/robot0_joint_pos_sin Tensor (7,) float64
langkah/pengamatan/robot0_joint_vel Tensor (7,) float64 kecepatan sambungan 7DOF
langkah/hadiah Tensor float64
langkah/keadaan Tensor (45,) float64
kereta Tensor bool
sah Tensor bool
lebih buruk Tensor bool
lebih buruk_lebih baik Tensor bool
lebih buruk_better_train Tensor bool
lebih buruk_lebih baik_valid Tensor bool
lebih buruk_oke Tensor bool
lebih buruk_oke_kereta Tensor bool
lebih buruk_oke_valid Tensor bool
bad_operator_1 Tensor bool
bad_operator_1_train Tensor bool
bad_operator_1_valid Tensor bool
bad_operator_2 Tensor bool
bad_operator_2_train Tensor bool
bad_operator_2_valid Tensor bool
kereta_buruk Tensor bool
lebih buruk_valid Tensor bool

robomimic_mh/transport_mh_image

  • Ukuran unduhan : 31.47 GiB

  • Ukuran dataset : 7.69 GiB

  • Di-cache otomatis ( dokumentasi ): Tidak

  • Struktur fitur :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(14,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'object': Tensor(shape=(41,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
            'robot1_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot1_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot1_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot1_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot1_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot1_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot1_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot1_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot1_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot1_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot1_joint_vel': Tensor(shape=(7,), dtype=float64),
            'shouldercamera0_image': Image(shape=(84, 84, 3), dtype=uint8),
            'shouldercamera1_image': Image(shape=(84, 84, 3), dtype=uint8),
        }),
        'reward': float64,
        'states': Tensor(shape=(115,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Dtype Keterangan
fiturDict
20_persen Tensor bool
20_persen_kereta Tensor bool
20_persen_valid Tensor bool
50 persen Tensor bool
50_persen_kereta Tensor bool
50_persen_valid Tensor bool
lebih baik Tensor bool
kereta_lebih baik Tensor bool
lebih baik_valid Tensor bool
episode_id Tensor rangkaian
cakrawala Tensor int32
Oke Tensor bool
oke_lebih baik Tensor bool
oke_better_train Tensor bool
oke_better_valid Tensor bool
oke_kereta Tensor bool
oke_valid Tensor bool
Langkah Himpunan data
langkah/tindakan Tensor (14,) float64
langkah/diskon Tensor int32
langkah/adalah_pertama Tensor bool
langkah/is_last Tensor bool
langkah/is_terminal Tensor bool
langkah/pengamatan fiturDict
langkah/pengamatan/objek Tensor (41,) float64
langkah/pengamatan/robot0_eef_pos Tensor (3,) float64 Posisi efektor akhir
langkah/pengamatan/robot0_eef_quat Tensor (4,) float64 Orientasi efektor akhir
langkah/pengamatan/robot0_eef_vel_ang Tensor (3,) float64 Kecepatan sudut end-effector
langkah/pengamatan/robot0_eef_vel_lin Tensor (3,) float64 kecepatan kartesius akhir-efektor
langkah/pengamatan/robot0_eye_in_hand_image Gambar (84, 84, 3) uint8
langkah/pengamatan/robot0_gripper_qpos Tensor (2,) float64 Posisi gripper
langkah/pengamatan/robot0_gripper_qvel Tensor (2,) float64 Kecepatan gripper
langkah/pengamatan/robot0_joint_pos Tensor (7,) float64 7DOF posisi bersama
langkah/pengamatan/robot0_joint_pos_cos Tensor (7,) float64
langkah/pengamatan/robot0_joint_pos_sin Tensor (7,) float64
langkah/pengamatan/robot0_joint_vel Tensor (7,) float64 kecepatan sambungan 7DOF
langkah/pengamatan/robot1_eef_pos Tensor (3,) float64 Posisi efektor akhir
langkah/pengamatan/robot1_eef_quat Tensor (4,) float64 Orientasi efektor akhir
langkah/pengamatan/robot1_eef_vel_ang Tensor (3,) float64 Kecepatan sudut end-effector
langkah/pengamatan/robot1_eef_vel_lin Tensor (3,) float64 kecepatan kartesius akhir-efektor
langkah/pengamatan/robot1_eye_in_hand_image Gambar (84, 84, 3) uint8
langkah/pengamatan/robot1_gripper_qpos Tensor (2,) float64 Posisi gripper
langkah/pengamatan/robot1_gripper_qvel Tensor (2,) float64 Kecepatan gripper
langkah/pengamatan/robot1_joint_pos Tensor (7,) float64 7DOF posisi bersama
langkah/pengamatan/robot1_joint_pos_cos Tensor (7,) float64
langkah/pengamatan/robot1_joint_pos_sin Tensor (7,) float64
langkah/pengamatan/robot1_joint_vel Tensor (7,) float64 kecepatan sambungan 7DOF
langkah/pengamatan/shouldercamera0_image Gambar (84, 84, 3) uint8
langkah/pengamatan/shouldercamera1_image Gambar (84, 84, 3) uint8
langkah/hadiah Tensor float64
langkah/keadaan Tensor (115,) float64
kereta Tensor bool
sah Tensor bool
lebih buruk Tensor bool
lebih buruk_lebih baik Tensor bool
lebih buruk_better_train Tensor bool
lebih buruk_lebih baik_valid Tensor bool
lebih buruk_oke Tensor bool
lebih buruk_oke_kereta Tensor bool
lebih buruk_oke_valid Tensor bool
kereta_buruk Tensor bool
lebih buruk_valid Tensor bool

robomimic_mh/transport_mh_low_dim

  • Ukuran unduhan : 607.47 MiB

  • Ukuran dataset : 434.43 MiB

  • Di-cache otomatis ( dokumentasi ): Tidak

  • Struktur fitur :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(14,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'object': Tensor(shape=(41,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
            'robot1_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot1_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot1_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot1_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot1_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot1_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot1_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot1_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot1_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot1_joint_vel': Tensor(shape=(7,), dtype=float64),
        }),
        'reward': float64,
        'states': Tensor(shape=(115,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Dtype Keterangan
fiturDict
20_persen Tensor bool
20_persen_kereta Tensor bool
20_persen_valid Tensor bool
50 persen Tensor bool
50_persen_kereta Tensor bool
50_persen_valid Tensor bool
lebih baik Tensor bool
kereta_lebih baik Tensor bool
lebih baik_valid Tensor bool
episode_id Tensor rangkaian
cakrawala Tensor int32
Oke Tensor bool
oke_lebih baik Tensor bool
oke_better_train Tensor bool
oke_better_valid Tensor bool
oke_kereta Tensor bool
oke_valid Tensor bool
Langkah Himpunan data
langkah/tindakan Tensor (14,) float64
langkah/diskon Tensor int32
langkah/adalah_pertama Tensor bool
langkah/is_last Tensor bool
langkah/is_terminal Tensor bool
langkah/pengamatan fiturDict
langkah/pengamatan/objek Tensor (41,) float64
langkah/pengamatan/robot0_eef_pos Tensor (3,) float64 Posisi efektor akhir
langkah/pengamatan/robot0_eef_quat Tensor (4,) float64 Orientasi efektor akhir
langkah/pengamatan/robot0_eef_vel_ang Tensor (3,) float64 Kecepatan sudut end-effector
langkah/pengamatan/robot0_eef_vel_lin Tensor (3,) float64 kecepatan kartesius akhir-efektor
langkah/pengamatan/robot0_gripper_qpos Tensor (2,) float64 Posisi gripper
langkah/pengamatan/robot0_gripper_qvel Tensor (2,) float64 Kecepatan gripper
langkah/pengamatan/robot0_joint_pos Tensor (7,) float64 7DOF posisi bersama
langkah/pengamatan/robot0_joint_pos_cos Tensor (7,) float64
langkah/pengamatan/robot0_joint_pos_sin Tensor (7,) float64
langkah/pengamatan/robot0_joint_vel Tensor (7,) float64 kecepatan sambungan 7DOF
langkah/pengamatan/robot1_eef_pos Tensor (3,) float64 Posisi efektor akhir
langkah/pengamatan/robot1_eef_quat Tensor (4,) float64 Orientasi efektor akhir
langkah/pengamatan/robot1_eef_vel_ang Tensor (3,) float64 Kecepatan sudut end-effector
langkah/pengamatan/robot1_eef_vel_lin Tensor (3,) float64 kecepatan kartesius akhir-efektor
langkah/pengamatan/robot1_gripper_qpos Tensor (2,) float64 Posisi gripper
langkah/pengamatan/robot1_gripper_qvel Tensor (2,) float64 Kecepatan gripper
langkah/pengamatan/robot1_joint_pos Tensor (7,) float64 7DOF posisi bersama
langkah/pengamatan/robot1_joint_pos_cos Tensor (7,) float64
langkah/pengamatan/robot1_joint_pos_sin Tensor (7,) float64
langkah/pengamatan/robot1_joint_vel Tensor (7,) float64 kecepatan sambungan 7DOF
langkah/hadiah Tensor float64
langkah/keadaan Tensor (115,) float64
kereta Tensor bool
sah Tensor bool
lebih buruk Tensor bool
lebih buruk_lebih baik Tensor bool
lebih buruk_better_train Tensor bool
lebih buruk_lebih baik_valid Tensor bool
lebih buruk_oke Tensor bool
lebih buruk_oke_kereta Tensor bool
lebih buruk_oke_valid Tensor bool
kereta_buruk Tensor bool
lebih buruk_valid Tensor bool
,

  • Deskripsi :

Kumpulan data manusia campuran Robomimic dikumpulkan oleh beberapa operator kemampuan campuran menggunakan platform RoboTurk . Setiap dataset terdiri dari 200 demonstrasi.

Setiap tugas memiliki dua versi: satu dengan pengamatan dimensi rendah ( low_dim ), dan satu lagi dengan gambar ( image ).

Kumpulan data mengikuti format RLDS untuk mewakili langkah dan episode.

Membelah Contoh
'train' 300
@inproceedings{robomimic2021,
  title={What Matters in Learning from Offline Human Demonstrations for Robot Manipulation},
  author={Ajay Mandlekar and Danfei Xu and Josiah Wong and Soroush Nasiriany
          and Chen Wang and Rohun Kulkarni and Li Fei-Fei and Silvio Savarese
          and Yuke Zhu and Roberto Mart\'{i}n-Mart\'{i}n},
  booktitle={Conference on Robot Learning},
  year={2021}
}

robomimic_mh/lift_mh_image (konfigurasi default)

  • Ukuran unduhan : 2.50 GiB

  • Ukuran dataset : 363.18 MiB

  • Di-cache otomatis ( dokumentasi ): Tidak

  • Struktur fitur :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_operator_1': bool,
    'better_operator_1_train': bool,
    'better_operator_1_valid': bool,
    'better_operator_2': bool,
    'better_operator_2_train': bool,
    'better_operator_2_valid': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_operator_1': bool,
    'okay_operator_1_train': bool,
    'okay_operator_1_valid': bool,
    'okay_operator_2': bool,
    'okay_operator_2_train': bool,
    'okay_operator_2_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'agentview_image': Image(shape=(84, 84, 3), dtype=uint8),
            'object': Tensor(shape=(10,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
        }),
        'reward': float64,
        'states': Tensor(shape=(32,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_operator_1': bool,
    'worse_operator_1_train': bool,
    'worse_operator_1_valid': bool,
    'worse_operator_2': bool,
    'worse_operator_2_train': bool,
    'worse_operator_2_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Dtype Keterangan
fiturDict
20_persen Tensor bool
20_persen_kereta Tensor bool
20_persen_valid Tensor bool
50 persen Tensor bool
50_persen_kereta Tensor bool
50_persen_valid Tensor bool
lebih baik Tensor bool
better_operator_1 Tensor bool
better_operator_1_train Tensor bool
better_operator_1_valid Tensor bool
better_operator_2 Tensor bool
better_operator_2_train Tensor bool
better_operator_2_valid Tensor bool
kereta_lebih baik Tensor bool
lebih baik_valid Tensor bool
episode_id Tensor rangkaian
cakrawala Tensor int32
Oke Tensor bool
oke_lebih baik Tensor bool
oke_better_train Tensor bool
oke_better_valid Tensor bool
oke_operator_1 Tensor bool
oke_operator_1_kereta Tensor bool
oke_operator_1_valid Tensor bool
oke_operator_2 Tensor bool
oke_operator_2_kereta Tensor bool
oke_operator_2_valid Tensor bool
oke_kereta Tensor bool
oke_valid Tensor bool
Langkah Himpunan data
langkah/tindakan Tensor (7,) float64
langkah/diskon Tensor int32
langkah/adalah_pertama Tensor bool
langkah/is_last Tensor bool
langkah/is_terminal Tensor bool
langkah/pengamatan fiturDict
langkah/pengamatan/agentview_image Gambar (84, 84, 3) uint8
langkah/pengamatan/objek Tensor (10,) float64
langkah/pengamatan/robot0_eef_pos Tensor (3,) float64 Posisi efektor akhir
langkah/pengamatan/robot0_eef_quat Tensor (4,) float64 Orientasi efektor akhir
langkah/pengamatan/robot0_eef_vel_ang Tensor (3,) float64 Kecepatan sudut end-effector
langkah/pengamatan/robot0_eef_vel_lin Tensor (3,) float64 kecepatan kartesius akhir-efektor
langkah/pengamatan/robot0_eye_in_hand_image Gambar (84, 84, 3) uint8
langkah/pengamatan/robot0_gripper_qpos Tensor (2,) float64 Posisi gripper
langkah/pengamatan/robot0_gripper_qvel Tensor (2,) float64 Kecepatan gripper
langkah/pengamatan/robot0_joint_pos Tensor (7,) float64 7DOF posisi bersama
langkah/pengamatan/robot0_joint_pos_cos Tensor (7,) float64
langkah/pengamatan/robot0_joint_pos_sin Tensor (7,) float64
langkah/pengamatan/robot0_joint_vel Tensor (7,) float64 kecepatan sambungan 7DOF
langkah/hadiah Tensor float64
langkah/keadaan Tensor (32,) float64
kereta Tensor bool
sah Tensor bool
lebih buruk Tensor bool
lebih buruk_lebih baik Tensor bool
lebih buruk_better_train Tensor bool
lebih buruk_lebih baik_valid Tensor bool
lebih buruk_oke Tensor bool
lebih buruk_oke_kereta Tensor bool
lebih buruk_oke_valid Tensor bool
bad_operator_1 Tensor bool
bad_operator_1_train Tensor bool
bad_operator_1_valid Tensor bool
bad_operator_2 Tensor bool
bad_operator_2_train Tensor bool
bad_operator_2_valid Tensor bool
kereta_buruk Tensor bool
lebih buruk_valid Tensor bool

robomimic_mh/lift_mh_low_dim

  • Ukuran unduhan : 45.73 MiB

  • Ukuran dataset : 27.26 MiB

  • Di-cache otomatis ( dokumentasi ): Ya

  • Struktur fitur :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_operator_1': bool,
    'better_operator_1_train': bool,
    'better_operator_1_valid': bool,
    'better_operator_2': bool,
    'better_operator_2_train': bool,
    'better_operator_2_valid': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_operator_1': bool,
    'okay_operator_1_train': bool,
    'okay_operator_1_valid': bool,
    'okay_operator_2': bool,
    'okay_operator_2_train': bool,
    'okay_operator_2_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'object': Tensor(shape=(10,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
        }),
        'reward': float64,
        'states': Tensor(shape=(32,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_operator_1': bool,
    'worse_operator_1_train': bool,
    'worse_operator_1_valid': bool,
    'worse_operator_2': bool,
    'worse_operator_2_train': bool,
    'worse_operator_2_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Dtype Keterangan
fiturDict
20_persen Tensor bool
20_persen_kereta Tensor bool
20_persen_valid Tensor bool
50 persen Tensor bool
50_persen_kereta Tensor bool
50_persen_valid Tensor bool
lebih baik Tensor bool
better_operator_1 Tensor bool
better_operator_1_train Tensor bool
better_operator_1_valid Tensor bool
better_operator_2 Tensor bool
better_operator_2_train Tensor bool
better_operator_2_valid Tensor bool
kereta_lebih baik Tensor bool
lebih baik_valid Tensor bool
episode_id Tensor rangkaian
cakrawala Tensor int32
Oke Tensor bool
oke_lebih baik Tensor bool
oke_better_train Tensor bool
oke_better_valid Tensor bool
oke_operator_1 Tensor bool
oke_operator_1_kereta Tensor bool
oke_operator_1_valid Tensor bool
oke_operator_2 Tensor bool
oke_operator_2_kereta Tensor bool
oke_operator_2_valid Tensor bool
oke_kereta Tensor bool
oke_valid Tensor bool
Langkah Himpunan data
langkah/tindakan Tensor (7,) float64
langkah/diskon Tensor int32
langkah/adalah_pertama Tensor bool
langkah/is_last Tensor bool
langkah/is_terminal Tensor bool
langkah/pengamatan fiturDict
langkah/pengamatan/objek Tensor (10,) float64
langkah/pengamatan/robot0_eef_pos Tensor (3,) float64 Posisi efektor akhir
langkah/pengamatan/robot0_eef_quat Tensor (4,) float64 Orientasi efektor akhir
langkah/pengamatan/robot0_eef_vel_ang Tensor (3,) float64 Kecepatan sudut end-effector
langkah/pengamatan/robot0_eef_vel_lin Tensor (3,) float64 kecepatan kartesius akhir-efektor
langkah/pengamatan/robot0_gripper_qpos Tensor (2,) float64 Posisi gripper
langkah/pengamatan/robot0_gripper_qvel Tensor (2,) float64 Kecepatan gripper
langkah/pengamatan/robot0_joint_pos Tensor (7,) float64 7DOF posisi bersama
langkah/pengamatan/robot0_joint_pos_cos Tensor (7,) float64
langkah/pengamatan/robot0_joint_pos_sin Tensor (7,) float64
langkah/pengamatan/robot0_joint_vel Tensor (7,) float64 kecepatan sambungan 7DOF
langkah/hadiah Tensor float64
langkah/keadaan Tensor (32,) float64
kereta Tensor bool
sah Tensor bool
lebih buruk Tensor bool
lebih buruk_lebih baik Tensor bool
lebih buruk_better_train Tensor bool
lebih buruk_lebih baik_valid Tensor bool
lebih buruk_oke Tensor bool
lebih buruk_oke_kereta Tensor bool
lebih buruk_oke_valid Tensor bool
bad_operator_1 Tensor bool
bad_operator_1_train Tensor bool
bad_operator_1_valid Tensor bool
bad_operator_2 Tensor bool
bad_operator_2_train Tensor bool
bad_operator_2_valid Tensor bool
kereta_buruk Tensor bool
lebih buruk_valid Tensor bool

robomimic_mh/can_mh_image

  • Ukuran unduhan : 5.05 GiB

  • Ukuran dataset : 1.23 GiB

  • Di-cache otomatis ( dokumentasi ): Tidak

  • Struktur fitur :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_operator_1': bool,
    'better_operator_1_train': bool,
    'better_operator_1_valid': bool,
    'better_operator_2': bool,
    'better_operator_2_train': bool,
    'better_operator_2_valid': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_operator_1': bool,
    'okay_operator_1_train': bool,
    'okay_operator_1_valid': bool,
    'okay_operator_2': bool,
    'okay_operator_2_train': bool,
    'okay_operator_2_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'agentview_image': Image(shape=(84, 84, 3), dtype=uint8),
            'object': Tensor(shape=(14,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
        }),
        'reward': float64,
        'states': Tensor(shape=(71,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_operator_1': bool,
    'worse_operator_1_train': bool,
    'worse_operator_1_valid': bool,
    'worse_operator_2': bool,
    'worse_operator_2_train': bool,
    'worse_operator_2_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Dtype Keterangan
fiturDict
20_persen Tensor bool
20_persen_kereta Tensor bool
20_persen_valid Tensor bool
50 persen Tensor bool
50_persen_kereta Tensor bool
50_persen_valid Tensor bool
lebih baik Tensor bool
better_operator_1 Tensor bool
better_operator_1_train Tensor bool
better_operator_1_valid Tensor bool
better_operator_2 Tensor bool
better_operator_2_train Tensor bool
better_operator_2_valid Tensor bool
kereta_lebih baik Tensor bool
lebih baik_valid Tensor bool
episode_id Tensor rangkaian
cakrawala Tensor int32
Oke Tensor bool
oke_lebih baik Tensor bool
oke_better_train Tensor bool
oke_better_valid Tensor bool
oke_operator_1 Tensor bool
oke_operator_1_kereta Tensor bool
oke_operator_1_valid Tensor bool
oke_operator_2 Tensor bool
oke_operator_2_kereta Tensor bool
oke_operator_2_valid Tensor bool
oke_kereta Tensor bool
oke_valid Tensor bool
Langkah Himpunan data
langkah/tindakan Tensor (7,) float64
langkah/diskon Tensor int32
langkah/adalah_pertama Tensor bool
langkah/is_last Tensor bool
langkah/is_terminal Tensor bool
langkah/pengamatan fiturDict
langkah/pengamatan/agentview_image Gambar (84, 84, 3) uint8
langkah/pengamatan/objek Tensor (14,) float64
langkah/pengamatan/robot0_eef_pos Tensor (3,) float64 Posisi efektor akhir
langkah/pengamatan/robot0_eef_quat Tensor (4,) float64 Orientasi efektor akhir
langkah/pengamatan/robot0_eef_vel_ang Tensor (3,) float64 Kecepatan sudut end-effector
langkah/pengamatan/robot0_eef_vel_lin Tensor (3,) float64 kecepatan kartesius akhir-efektor
langkah/pengamatan/robot0_eye_in_hand_image Gambar (84, 84, 3) uint8
langkah/pengamatan/robot0_gripper_qpos Tensor (2,) float64 Posisi gripper
langkah/pengamatan/robot0_gripper_qvel Tensor (2,) float64 Kecepatan gripper
langkah/pengamatan/robot0_joint_pos Tensor (7,) float64 7DOF posisi bersama
langkah/pengamatan/robot0_joint_pos_cos Tensor (7,) float64
langkah/pengamatan/robot0_joint_pos_sin Tensor (7,) float64
langkah/pengamatan/robot0_joint_vel Tensor (7,) float64 kecepatan sambungan 7DOF
langkah/hadiah Tensor float64
langkah/keadaan Tensor (71,) float64
kereta Tensor bool
sah Tensor bool
lebih buruk Tensor bool
lebih buruk_lebih baik Tensor bool
lebih buruk_better_train Tensor bool
lebih buruk_lebih baik_valid Tensor bool
lebih buruk_oke Tensor bool
lebih buruk_oke_kereta Tensor bool
lebih buruk_oke_valid Tensor bool
bad_operator_1 Tensor bool
bad_operator_1_train Tensor bool
bad_operator_1_valid Tensor bool
bad_operator_2 Tensor bool
bad_operator_2_train Tensor bool
bad_operator_2_valid Tensor bool
kereta_buruk Tensor bool
lebih buruk_valid Tensor bool

robomimic_mh/can_mh_low_dim

  • Ukuran unduhan : 107.28 MiB

  • Ukuran dataset : 75.19 MiB

  • Di-cache otomatis ( dokumentasi ): Ya

  • Struktur fitur :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_operator_1': bool,
    'better_operator_1_train': bool,
    'better_operator_1_valid': bool,
    'better_operator_2': bool,
    'better_operator_2_train': bool,
    'better_operator_2_valid': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_operator_1': bool,
    'okay_operator_1_train': bool,
    'okay_operator_1_valid': bool,
    'okay_operator_2': bool,
    'okay_operator_2_train': bool,
    'okay_operator_2_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'object': Tensor(shape=(14,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
        }),
        'reward': float64,
        'states': Tensor(shape=(71,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_operator_1': bool,
    'worse_operator_1_train': bool,
    'worse_operator_1_valid': bool,
    'worse_operator_2': bool,
    'worse_operator_2_train': bool,
    'worse_operator_2_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Dtype Keterangan
fiturDict
20_persen Tensor bool
20_persen_kereta Tensor bool
20_persen_valid Tensor bool
50 persen Tensor bool
50_persen_kereta Tensor bool
50_persen_valid Tensor bool
lebih baik Tensor bool
better_operator_1 Tensor bool
better_operator_1_train Tensor bool
better_operator_1_valid Tensor bool
better_operator_2 Tensor bool
better_operator_2_train Tensor bool
better_operator_2_valid Tensor bool
kereta_lebih baik Tensor bool
lebih baik_valid Tensor bool
episode_id Tensor rangkaian
cakrawala Tensor int32
Oke Tensor bool
oke_lebih baik Tensor bool
oke_better_train Tensor bool
oke_better_valid Tensor bool
oke_operator_1 Tensor bool
oke_operator_1_kereta Tensor bool
oke_operator_1_valid Tensor bool
oke_operator_2 Tensor bool
oke_operator_2_kereta Tensor bool
oke_operator_2_valid Tensor bool
oke_kereta Tensor bool
oke_valid Tensor bool
Langkah Himpunan data
langkah/tindakan Tensor (7,) float64
langkah/diskon Tensor int32
langkah/adalah_pertama Tensor bool
langkah/is_last Tensor bool
langkah/is_terminal Tensor bool
langkah/pengamatan fiturDict
langkah/pengamatan/objek Tensor (14,) float64
langkah/pengamatan/robot0_eef_pos Tensor (3,) float64 Posisi efektor akhir
langkah/pengamatan/robot0_eef_quat Tensor (4,) float64 Orientasi efektor akhir
langkah/pengamatan/robot0_eef_vel_ang Tensor (3,) float64 Kecepatan sudut end-effector
langkah/pengamatan/robot0_eef_vel_lin Tensor (3,) float64 kecepatan kartesius akhir-efektor
langkah/pengamatan/robot0_gripper_qpos Tensor (2,) float64 Posisi gripper
langkah/pengamatan/robot0_gripper_qvel Tensor (2,) float64 Kecepatan gripper
langkah/pengamatan/robot0_joint_pos Tensor (7,) float64 7DOF posisi bersama
langkah/pengamatan/robot0_joint_pos_cos Tensor (7,) float64
langkah/pengamatan/robot0_joint_pos_sin Tensor (7,) float64
langkah/pengamatan/robot0_joint_vel Tensor (7,) float64 kecepatan sambungan 7DOF
langkah/hadiah Tensor float64
langkah/keadaan Tensor (71,) float64
kereta Tensor bool
sah Tensor bool
lebih buruk Tensor bool
lebih buruk_lebih baik Tensor bool
lebih buruk_better_train Tensor bool
lebih buruk_lebih baik_valid Tensor bool
lebih buruk_oke Tensor bool
lebih buruk_oke_kereta Tensor bool
lebih buruk_oke_valid Tensor bool
bad_operator_1 Tensor bool
bad_operator_1_train Tensor bool
bad_operator_1_valid Tensor bool
bad_operator_2 Tensor bool
bad_operator_2_train Tensor bool
bad_operator_2_valid Tensor bool
kereta_buruk Tensor bool
lebih buruk_valid Tensor bool

robomimic_mh/square_mh_image

  • Ukuran unduhan : 6.48 GiB

  • Ukuran dataset : 1.07 GiB

  • Di-cache otomatis ( dokumentasi ): Tidak

  • Struktur fitur :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_operator_1': bool,
    'better_operator_1_train': bool,
    'better_operator_1_valid': bool,
    'better_operator_2': bool,
    'better_operator_2_train': bool,
    'better_operator_2_valid': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_operator_1': bool,
    'okay_operator_1_train': bool,
    'okay_operator_1_valid': bool,
    'okay_operator_2': bool,
    'okay_operator_2_train': bool,
    'okay_operator_2_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'agentview_image': Image(shape=(84, 84, 3), dtype=uint8),
            'object': Tensor(shape=(14,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
        }),
        'reward': float64,
        'states': Tensor(shape=(45,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_operator_1': bool,
    'worse_operator_1_train': bool,
    'worse_operator_1_valid': bool,
    'worse_operator_2': bool,
    'worse_operator_2_train': bool,
    'worse_operator_2_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Dokumentasi fitur :
Fitur Kelas Membentuk Dtype Keterangan
fiturDict
20_persen Tensor bool
20_persen_kereta Tensor bool
20_persen_valid Tensor bool
50 persen Tensor bool
50_persen_kereta Tensor bool
50_persen_valid Tensor bool
lebih baik Tensor bool
better_operator_1 Tensor bool
better_operator_1_train Tensor bool
better_operator_1_valid Tensor bool
better_operator_2 Tensor bool
better_operator_2_train Tensor bool
better_operator_2_valid Tensor bool
better_train Tensor bool
better_valid Tensor bool
episode_id Tensor string
horizon Tensor int32
okay Tensor bool
okay_better Tensor bool
okay_better_train Tensor bool
okay_better_valid Tensor bool
okay_operator_1 Tensor bool
okay_operator_1_train Tensor bool
okay_operator_1_valid Tensor bool
okay_operator_2 Tensor bool
okay_operator_2_train Tensor bool
okay_operator_2_valid Tensor bool
okay_train Tensor bool
okay_valid Tensor bool
steps Dataset
steps/action Tensor (7,) float64
steps/discount Tensor int32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation FeaturesDict
steps/observation/agentview_image Image (84, 84, 3) uint8
steps/observation/object Tensor (14,) float64
steps/observation/robot0_eef_pos Tensor (3,) float64 End-effector position
steps/observation/robot0_eef_quat Tensor (4,) float64 End-effector orientation
steps/observation/robot0_eef_vel_ang Tensor (3,) float64 End-effector angular velocity
steps/observation/robot0_eef_vel_lin Tensor (3,) float64 End-effector cartesian velocity
steps/observation/robot0_eye_in_hand_image Image (84, 84, 3) uint8
steps/observation/robot0_gripper_qpos Tensor (2,) float64 Gripper position
steps/observation/robot0_gripper_qvel Tensor (2,) float64 Gripper velocity
steps/observation/robot0_joint_pos Tensor (7,) float64 7DOF joint positions
steps/observation/robot0_joint_pos_cos Tensor (7,) float64
steps/observation/robot0_joint_pos_sin Tensor (7,) float64
steps/observation/robot0_joint_vel Tensor (7,) float64 7DOF joint velocities
steps/reward Tensor float64
steps/states Tensor (45,) float64
train Tensor bool
valid Tensor bool
worse Tensor bool
worse_better Tensor bool
worse_better_train Tensor bool
worse_better_valid Tensor bool
worse_okay Tensor bool
worse_okay_train Tensor bool
worse_okay_valid Tensor bool
worse_operator_1 Tensor bool
worse_operator_1_train Tensor bool
worse_operator_1_valid Tensor bool
worse_operator_2 Tensor bool
worse_operator_2_train Tensor bool
worse_operator_2_valid Tensor bool
worse_train Tensor bool
worse_valid Tensor bool

robomimic_mh/square_mh_low_dim

  • Download size : 118.13 MiB

  • Dataset size : 80.37 MiB

  • Auto-cached ( documentation ): Yes

  • Feature structure :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_operator_1': bool,
    'better_operator_1_train': bool,
    'better_operator_1_valid': bool,
    'better_operator_2': bool,
    'better_operator_2_train': bool,
    'better_operator_2_valid': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_operator_1': bool,
    'okay_operator_1_train': bool,
    'okay_operator_1_valid': bool,
    'okay_operator_2': bool,
    'okay_operator_2_train': bool,
    'okay_operator_2_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'object': Tensor(shape=(14,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
        }),
        'reward': float64,
        'states': Tensor(shape=(45,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_operator_1': bool,
    'worse_operator_1_train': bool,
    'worse_operator_1_valid': bool,
    'worse_operator_2': bool,
    'worse_operator_2_train': bool,
    'worse_operator_2_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Feature documentation :
Feature Class Shape Dtype Description
FeaturesDict
20_percent Tensor bool
20_percent_train Tensor bool
20_percent_valid Tensor bool
50_percent Tensor bool
50_percent_train Tensor bool
50_percent_valid Tensor bool
better Tensor bool
better_operator_1 Tensor bool
better_operator_1_train Tensor bool
better_operator_1_valid Tensor bool
better_operator_2 Tensor bool
better_operator_2_train Tensor bool
better_operator_2_valid Tensor bool
better_train Tensor bool
better_valid Tensor bool
episode_id Tensor string
horizon Tensor int32
okay Tensor bool
okay_better Tensor bool
okay_better_train Tensor bool
okay_better_valid Tensor bool
okay_operator_1 Tensor bool
okay_operator_1_train Tensor bool
okay_operator_1_valid Tensor bool
okay_operator_2 Tensor bool
okay_operator_2_train Tensor bool
okay_operator_2_valid Tensor bool
okay_train Tensor bool
okay_valid Tensor bool
steps Dataset
steps/action Tensor (7,) float64
steps/discount Tensor int32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation FeaturesDict
steps/observation/object Tensor (14,) float64
steps/observation/robot0_eef_pos Tensor (3,) float64 End-effector position
steps/observation/robot0_eef_quat Tensor (4,) float64 End-effector orientation
steps/observation/robot0_eef_vel_ang Tensor (3,) float64 End-effector angular velocity
steps/observation/robot0_eef_vel_lin Tensor (3,) float64 End-effector cartesian velocity
steps/observation/robot0_gripper_qpos Tensor (2,) float64 Gripper position
steps/observation/robot0_gripper_qvel Tensor (2,) float64 Gripper velocity
steps/observation/robot0_joint_pos Tensor (7,) float64 7DOF joint positions
steps/observation/robot0_joint_pos_cos Tensor (7,) float64
steps/observation/robot0_joint_pos_sin Tensor (7,) float64
steps/observation/robot0_joint_vel Tensor (7,) float64 7DOF joint velocities
steps/reward Tensor float64
steps/states Tensor (45,) float64
train Tensor bool
valid Tensor bool
worse Tensor bool
worse_better Tensor bool
worse_better_train Tensor bool
worse_better_valid Tensor bool
worse_okay Tensor bool
worse_okay_train Tensor bool
worse_okay_valid Tensor bool
worse_operator_1 Tensor bool
worse_operator_1_train Tensor bool
worse_operator_1_valid Tensor bool
worse_operator_2 Tensor bool
worse_operator_2_train Tensor bool
worse_operator_2_valid Tensor bool
worse_train Tensor bool
worse_valid Tensor bool

robomimic_mh/transport_mh_image

  • Download size : 31.47 GiB

  • Dataset size : 7.69 GiB

  • Auto-cached ( documentation ): No

  • Feature structure :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(14,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'object': Tensor(shape=(41,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
            'robot1_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot1_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot1_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot1_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot1_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot1_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot1_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot1_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot1_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot1_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot1_joint_vel': Tensor(shape=(7,), dtype=float64),
            'shouldercamera0_image': Image(shape=(84, 84, 3), dtype=uint8),
            'shouldercamera1_image': Image(shape=(84, 84, 3), dtype=uint8),
        }),
        'reward': float64,
        'states': Tensor(shape=(115,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Feature documentation :
Feature Class Shape Dtype Description
FeaturesDict
20_percent Tensor bool
20_percent_train Tensor bool
20_percent_valid Tensor bool
50_percent Tensor bool
50_percent_train Tensor bool
50_percent_valid Tensor bool
better Tensor bool
better_train Tensor bool
better_valid Tensor bool
episode_id Tensor string
horizon Tensor int32
okay Tensor bool
okay_better Tensor bool
okay_better_train Tensor bool
okay_better_valid Tensor bool
okay_train Tensor bool
okay_valid Tensor bool
steps Dataset
steps/action Tensor (14,) float64
steps/discount Tensor int32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation FeaturesDict
steps/observation/object Tensor (41,) float64
steps/observation/robot0_eef_pos Tensor (3,) float64 End-effector position
steps/observation/robot0_eef_quat Tensor (4,) float64 End-effector orientation
steps/observation/robot0_eef_vel_ang Tensor (3,) float64 End-effector angular velocity
steps/observation/robot0_eef_vel_lin Tensor (3,) float64 End-effector cartesian velocity
steps/observation/robot0_eye_in_hand_image Image (84, 84, 3) uint8
steps/observation/robot0_gripper_qpos Tensor (2,) float64 Gripper position
steps/observation/robot0_gripper_qvel Tensor (2,) float64 Gripper velocity
steps/observation/robot0_joint_pos Tensor (7,) float64 7DOF joint positions
steps/observation/robot0_joint_pos_cos Tensor (7,) float64
steps/observation/robot0_joint_pos_sin Tensor (7,) float64
steps/observation/robot0_joint_vel Tensor (7,) float64 7DOF joint velocities
steps/observation/robot1_eef_pos Tensor (3,) float64 End-effector position
steps/observation/robot1_eef_quat Tensor (4,) float64 End-effector orientation
steps/observation/robot1_eef_vel_ang Tensor (3,) float64 End-effector angular velocity
steps/observation/robot1_eef_vel_lin Tensor (3,) float64 End-effector cartesian velocity
steps/observation/robot1_eye_in_hand_image Image (84, 84, 3) uint8
steps/observation/robot1_gripper_qpos Tensor (2,) float64 Gripper position
steps/observation/robot1_gripper_qvel Tensor (2,) float64 Gripper velocity
steps/observation/robot1_joint_pos Tensor (7,) float64 7DOF joint positions
steps/observation/robot1_joint_pos_cos Tensor (7,) float64
steps/observation/robot1_joint_pos_sin Tensor (7,) float64
steps/observation/robot1_joint_vel Tensor (7,) float64 7DOF joint velocities
steps/observation/shouldercamera0_image Image (84, 84, 3) uint8
steps/observation/shouldercamera1_image Image (84, 84, 3) uint8
steps/reward Tensor float64
steps/states Tensor (115,) float64
train Tensor bool
valid Tensor bool
worse Tensor bool
worse_better Tensor bool
worse_better_train Tensor bool
worse_better_valid Tensor bool
worse_okay Tensor bool
worse_okay_train Tensor bool
worse_okay_valid Tensor bool
worse_train Tensor bool
worse_valid Tensor bool

robomimic_mh/transport_mh_low_dim

  • Download size : 607.47 MiB

  • Dataset size : 434.43 MiB

  • Auto-cached ( documentation ): No

  • Feature structure :

FeaturesDict({
    '20_percent': bool,
    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
    'better_train': bool,
    'better_valid': bool,
    'episode_id': string,
    'horizon': int32,
    'okay': bool,
    'okay_better': bool,
    'okay_better_train': bool,
    'okay_better_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
        'action': Tensor(shape=(14,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'object': Tensor(shape=(41,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64),
            'robot1_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot1_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot1_eef_vel_ang': Tensor(shape=(3,), dtype=float64),
            'robot1_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
            'robot1_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot1_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            'robot1_joint_pos': Tensor(shape=(7,), dtype=float64),
            'robot1_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot1_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot1_joint_vel': Tensor(shape=(7,), dtype=float64),
        }),
        'reward': float64,
        'states': Tensor(shape=(115,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
    'worse_okay': bool,
    'worse_okay_train': bool,
    'worse_okay_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})
  • Feature documentation :
Feature Class Shape Dtype Description
FeaturesDict
20_percent Tensor bool
20_percent_train Tensor bool
20_percent_valid Tensor bool
50_percent Tensor bool
50_percent_train Tensor bool
50_percent_valid Tensor bool
better Tensor bool
better_train Tensor bool
better_valid Tensor bool
episode_id Tensor string
horizon Tensor int32
okay Tensor bool
okay_better Tensor bool
okay_better_train Tensor bool
okay_better_valid Tensor bool
okay_train Tensor bool
okay_valid Tensor bool
steps Dataset
steps/action Tensor (14,) float64
steps/discount Tensor int32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation FeaturesDict
steps/observation/object Tensor (41,) float64
steps/observation/robot0_eef_pos Tensor (3,) float64 End-effector position
steps/observation/robot0_eef_quat Tensor (4,) float64 End-effector orientation
steps/observation/robot0_eef_vel_ang Tensor (3,) float64 End-effector angular velocity
steps/observation/robot0_eef_vel_lin Tensor (3,) float64 End-effector cartesian velocity
steps/observation/robot0_gripper_qpos Tensor (2,) float64 Gripper position
steps/observation/robot0_gripper_qvel Tensor (2,) float64 Gripper velocity
steps/observation/robot0_joint_pos Tensor (7,) float64 7DOF joint positions
steps/observation/robot0_joint_pos_cos Tensor (7,) float64
steps/observation/robot0_joint_pos_sin Tensor (7,) float64
steps/observation/robot0_joint_vel Tensor (7,) float64 7DOF joint velocities
steps/observation/robot1_eef_pos Tensor (3,) float64 End-effector position
steps/observation/robot1_eef_quat Tensor (4,) float64 End-effector orientation
steps/observation/robot1_eef_vel_ang Tensor (3,) float64 End-effector angular velocity
steps/observation/robot1_eef_vel_lin Tensor (3,) float64 End-effector cartesian velocity
steps/observation/robot1_gripper_qpos Tensor (2,) float64 Gripper position
steps/observation/robot1_gripper_qvel Tensor (2,) float64 Gripper velocity
steps/observation/robot1_joint_pos Tensor (7,) float64 7DOF joint positions
steps/observation/robot1_joint_pos_cos Tensor (7,) float64
steps/observation/robot1_joint_pos_sin Tensor (7,) float64
steps/observation/robot1_joint_vel Tensor (7,) float64 7DOF joint velocities
steps/reward Tensor float64
steps/states Tensor (115,) float64
train Tensor bool
valid Tensor bool
worse Tensor bool
worse_better Tensor bool
worse_better_train Tensor bool
worse_better_valid Tensor bool
worse_okay Tensor bool
worse_okay_train Tensor bool
worse_okay_valid Tensor bool
worse_train Tensor bool
worse_valid Tensor bool