robomimic_mh

• বর্ণনা :

Robomimic মিশ্র মানব ডেটাসেট RoboTurk প্ল্যাটফর্ম ব্যবহার করে বেশ কয়েকটি মিশ্র-ক্ষমতা অপারেটর দ্বারা সংগ্রহ করা হয়েছিল। প্রতিটি ডেটাসেট 200টি প্রদর্শন নিয়ে গঠিত।

প্রতিটি টাস্কের দুটি সংস্করণ রয়েছে: একটি নিম্ন মাত্রিক পর্যবেক্ষণ সহ ( low_dim ), এবং একটি চিত্র সহ ( image )।

ডেটাসেটগুলি ধাপ এবং পর্বগুলি উপস্থাপন করতে RLDS বিন্যাস অনুসরণ করে।

• হোমপেজ : https://arise-initiative.github.io/robomimic-web/

• সোর্স কোড : tfds.datasets.robomimic_mh.Builder

• সংস্করণ :

  • 1.0.0 (ডিফল্ট): প্রাথমিক প্রকাশ।

• বিভাজন :

• তত্ত্বাবধান করা কী (দেখুন as_supervised doc ): None

• চিত্র ( tfds.show_examples ): সমর্থিত নয়।

• উদাহরণ ( tfds.as_dataframe ): অনুপস্থিত।

• উদ্ধৃতি :

@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 (ডিফল্ট কনফিগারেশন)

• ডাউনলোড সাইজ : 2.50 GiB

• ডেটাসেটের আকার : 363.18 MiB

• স্বয়ংক্রিয় ক্যাশে ( ডকুমেন্টেশন ): না

• বৈশিষ্ট্য গঠন :

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, description=End-effector position),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64, description=End-effector orientation),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64, description=End-effector angular velocity),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64, description=End-effector cartesian velocity),
            'robot0_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64, description=Gripper position),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64, description=Gripper velocity),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64, description=7DOF joint positions),
            '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, description=7DOF joint velocities),
        }),
        '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,
})

• বৈশিষ্ট্য ডকুমেন্টেশন :

robomimic_mh/lift_mh_low_dim

• ডাউনলোড সাইজঃ 45.73 MiB

• ডেটাসেটের আকার : 27.26 MiB

• স্বয়ংক্রিয় ক্যাশে ( ডকুমেন্টেশন ): হ্যাঁ

• বৈশিষ্ট্য গঠন :

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, description=End-effector position),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64, description=End-effector orientation),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64, description=End-effector angular velocity),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64, description=End-effector cartesian velocity),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64, description=Gripper position),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64, description=Gripper velocity),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64, description=7DOF joint positions),
            '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, description=7DOF joint velocities),
        }),
        '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,
})

• বৈশিষ্ট্য ডকুমেন্টেশন :

robomimic_mh/can_mh_image

• ডাউনলোড সাইজ : 5.05 GiB

• ডেটাসেটের আকার : 1.23 GiB

• স্বয়ংক্রিয় ক্যাশে ( ডকুমেন্টেশন ): না

• বৈশিষ্ট্য গঠন :

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, description=End-effector position),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64, description=End-effector orientation),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64, description=End-effector angular velocity),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64, description=End-effector cartesian velocity),
            'robot0_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64, description=Gripper position),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64, description=Gripper velocity),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64, description=7DOF joint positions),
            '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, description=7DOF joint velocities),
        }),
        '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,
})

• বৈশিষ্ট্য ডকুমেন্টেশন :

robomimic_mh/can_mh_low_dim

• ডাউনলোড সাইজঃ 107.28 MiB

• ডেটাসেটের আকার : 75.19 MiB

• স্বয়ংক্রিয় ক্যাশে ( ডকুমেন্টেশন ): হ্যাঁ

• বৈশিষ্ট্য গঠন :

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, description=End-effector position),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64, description=End-effector orientation),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64, description=End-effector angular velocity),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64, description=End-effector cartesian velocity),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64, description=Gripper position),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64, description=Gripper velocity),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64, description=7DOF joint positions),
            '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, description=7DOF joint velocities),
        }),
        '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,
})

• বৈশিষ্ট্য ডকুমেন্টেশন :

রোবোমিমিক_এমএইচ/বর্গ_এমএইচ_ছবি

• ডাউনলোড সাইজ : 6.48 GiB

• ডেটাসেটের আকার : 1.07 GiB

• স্বয়ংক্রিয় ক্যাশে ( ডকুমেন্টেশন ): না

• বৈশিষ্ট্য গঠন :

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, description=End-effector position),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64, description=End-effector orientation),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64, description=End-effector angular velocity),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64, description=End-effector cartesian velocity),
            'robot0_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64, description=Gripper position),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64, description=Gripper velocity),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64, description=7DOF joint positions),
            '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, description=7DOF joint velocities),
        }),
        '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,
})

• বৈশিষ্ট্য ডকুমেন্টেশন :

রোবোমিমিক_এমএইচ/বর্গ_এমএইচ_লো_ডিম

• ডাউনলোড সাইজ : 118.13 MiB

• ডেটাসেটের আকার : 80.37 MiB

• স্বয়ংক্রিয় ক্যাশে ( ডকুমেন্টেশন ): হ্যাঁ

• বৈশিষ্ট্য গঠন :

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, description=End-effector position),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64, description=End-effector orientation),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64, description=End-effector angular velocity),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64, description=End-effector cartesian velocity),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64, description=Gripper position),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64, description=Gripper velocity),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64, description=7DOF joint positions),
            '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, description=7DOF joint velocities),
        }),
        '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,
})

• বৈশিষ্ট্য ডকুমেন্টেশন :

রোবোমিমিক_এমএইচ/ট্রান্সপোর্ট_এমএইচ_ছবি

• ডাউনলোড আকার : 31.47 GiB

• ডেটাসেটের আকার : 7.69 GiB

• স্বয়ংক্রিয় ক্যাশে ( ডকুমেন্টেশন ): না

• বৈশিষ্ট্য গঠন :

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, description=End-effector position),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64, description=End-effector orientation),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64, description=End-effector angular velocity),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64, description=End-effector cartesian velocity),
            'robot0_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64, description=Gripper position),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64, description=Gripper velocity),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64, description=7DOF joint positions),
            '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, description=7DOF joint velocities),
            'robot1_eef_pos': Tensor(shape=(3,), dtype=float64, description=End-effector position),
            'robot1_eef_quat': Tensor(shape=(4,), dtype=float64, description=End-effector orientation),
            'robot1_eef_vel_ang': Tensor(shape=(3,), dtype=float64, description=End-effector angular velocity),
            'robot1_eef_vel_lin': Tensor(shape=(3,), dtype=float64, description=End-effector cartesian velocity),
            'robot1_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot1_gripper_qpos': Tensor(shape=(2,), dtype=float64, description=Gripper position),
            'robot1_gripper_qvel': Tensor(shape=(2,), dtype=float64, description=Gripper velocity),
            'robot1_joint_pos': Tensor(shape=(7,), dtype=float64, description=7DOF joint positions),
            '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, description=7DOF joint velocities),
            '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,
})

• বৈশিষ্ট্য ডকুমেন্টেশন :

রোবোমিমিক_এমএইচ/ট্রান্সপোর্ট_এমএইচ_লো_ডিম

• ডাউনলোড সাইজ : 607.47 MiB

• ডেটাসেটের আকার : 434.43 MiB

• স্বয়ংক্রিয় ক্যাশে ( ডকুমেন্টেশন ): না

• বৈশিষ্ট্য গঠন :

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, description=End-effector position),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64, description=End-effector orientation),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64, description=End-effector angular velocity),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64, description=End-effector cartesian velocity),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64, description=Gripper position),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64, description=Gripper velocity),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64, description=7DOF joint positions),
            '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, description=7DOF joint velocities),
            'robot1_eef_pos': Tensor(shape=(3,), dtype=float64, description=End-effector position),
            'robot1_eef_quat': Tensor(shape=(4,), dtype=float64, description=End-effector orientation),
            'robot1_eef_vel_ang': Tensor(shape=(3,), dtype=float64, description=End-effector angular velocity),
            'robot1_eef_vel_lin': Tensor(shape=(3,), dtype=float64, description=End-effector cartesian velocity),
            'robot1_gripper_qpos': Tensor(shape=(2,), dtype=float64, description=Gripper position),
            'robot1_gripper_qvel': Tensor(shape=(2,), dtype=float64, description=Gripper velocity),
            'robot1_joint_pos': Tensor(shape=(7,), dtype=float64, description=7DOF joint positions),
            '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, description=7DOF joint velocities),
        }),
        '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,
})

• বৈশিষ্ট্য ডকুমেন্টেশন :