robomimic_mh

• Descrição :

Os conjuntos de dados humanos mistos do Robomimic foram coletados por vários operadores de habilidades mistas usando a plataforma RoboTurk . Cada conjunto de dados consiste em 200 demonstrações.

Cada tarefa tem duas versões: uma com observações de baixa dimensão ( low_dim ) e outra com imagens ( image ).

Os conjuntos de dados seguem o formato RLDS para representar etapas e episódios.

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

• Código-fonte : tfds.datasets.robomimic_mh.Builder

• Versões :

  • 1.0.0 (padrão): versão inicial.

• Divisões :

• Chaves supervisionadas (Consulte as_supervised doc ): None

• Figura ( tfds.show_examples ): Não compatível.

• Exemplos ( tfds.as_dataframe ): Ausente.

• Citação :

@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 (configuração padrão)

• Tamanho do download : 2.50 GiB

• Tamanho do conjunto de dados : 363.18 MiB

• Armazenado em cache automaticamente ( documentação ): Não

• Estrutura de recursos :

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,
})

• Documentação do recurso :

robomimic_mh/lift_mh_low_dim

• Tamanho do download : 45.73 MiB

• Tamanho do conjunto de dados : 27.26 MiB

• Cache automático ( documentação ): Sim

• Estrutura de recursos :

FeaturesDict({
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    '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,
})

• Documentação do recurso :

robomimic_mh/can_mh_image

• Tamanho do download : 5.05 GiB

• Tamanho do conjunto de dados : 1.23 GiB

• Armazenado em cache automaticamente ( documentação ): Não

• Estrutura de recursos :

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,
})

• Documentação do recurso :

robomimic_mh/can_mh_low_dim

• Tamanho do download : 107.28 MiB

• Tamanho do conjunto de dados : 75.19 MiB

• Cache automático ( documentação ): Sim

• Estrutura de recursos :

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,
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    '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,
})

• Documentação do recurso :

robomimic_mh/square_mh_image

• Tamanho do download : 6.48 GiB

• Tamanho do conjunto de dados : 1.07 GiB

• Armazenado em cache automaticamente ( documentação ): Não

• Estrutura de recursos :

FeaturesDict({
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    '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,
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    '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,
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    '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,
})

• Documentação do recurso :

robomimic_mh/square_mh_low_dim

• Tamanho do download : 118.13 MiB

• Tamanho do conjunto de dados : 80.37 MiB

• Cache automático ( documentação ): Sim

• Estrutura de recursos :

FeaturesDict({
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    '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({
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            '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),
        }),
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        'states': Tensor(shape=(45,), dtype=float64),
    }),
    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
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    '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,
})

• Documentação do recurso :

robomimic_mh/transport_mh_image

• Tamanho do download : 31.47 GiB

• Tamanho do conjunto de dados : 7.69 GiB

• Armazenado em cache automaticamente ( documentação ): Não

• Estrutura de recursos :

FeaturesDict({
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    '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({
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            '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,
})

• Documentação do recurso :

robomimic_mh/transport_mh_low_dim

• Tamanho do download : 607.47 MiB

• Tamanho do conjunto de dados : 434.43 MiB

• Armazenado em cache automaticamente ( documentação ): Não

• Estrutura de recursos :

FeaturesDict({
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    '20_percent_train': bool,
    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
    '50_percent_valid': bool,
    'better': bool,
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        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
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            '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),
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            '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),
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            '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,
})

• Documentação do recurso :

• Descrição :

Os conjuntos de dados humanos mistos do Robomimic foram coletados por vários operadores de habilidades mistas usando a plataforma RoboTurk . Cada conjunto de dados consiste em 200 demonstrações.

Cada tarefa tem duas versões: uma com observações de baixa dimensão ( low_dim ) e outra com imagens ( image ).

Os conjuntos de dados seguem o formato RLDS para representar etapas e episódios.

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

• Código-fonte : tfds.datasets.robomimic_mh.Builder

• Versões :

  • 1.0.0 (padrão): versão inicial.

• Divisões :

• Chaves supervisionadas (Consulte as_supervised doc ): None

• Figura ( tfds.show_examples ): Não suportado.

• Exemplos ( tfds.as_dataframe ): Ausente.

• Citação :

@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 (configuração padrão)

• Tamanho do download : 2.50 GiB

• Tamanho do conjunto de dados : 363.18 MiB

• Armazenado em cache automaticamente ( documentação ): Não

• Estrutura de recursos :

FeaturesDict({
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    '20_percent_valid': bool,
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    '50_percent_train': bool,
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    'better': bool,
    'better_operator_1': bool,
    'better_operator_1_train': bool,
    'better_operator_1_valid': bool,
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    'okay_operator_2_valid': bool,
    'okay_train': bool,
    'okay_valid': bool,
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        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
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            '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),
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    'train': bool,
    'valid': bool,
    'worse': bool,
    'worse_better': bool,
    'worse_better_train': bool,
    'worse_better_valid': bool,
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    '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,
})

• Documentação do recurso :

robomimic_mh/lift_mh_low_dim

• Tamanho do download : 45.73 MiB

• Tamanho do conjunto de dados : 27.26 MiB

• Cache automático ( documentação ): Sim

• Estrutura de recursos :

FeaturesDict({
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    '20_percent_valid': bool,
    '50_percent': bool,
    '50_percent_train': bool,
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            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
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    'worse_operator_2_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})

• Documentação do recurso :

robomimic_mh/can_mh_image

• Tamanho do download : 5.05 GiB

• Tamanho do conjunto de dados : 1.23 GiB

• Armazenado em cache automaticamente ( documentação ): Não

• Estrutura de recursos :

FeaturesDict({
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            '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),
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        'reward': float64,
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    'worse_operator_2_valid': bool,
    'worse_train': bool,
    'worse_valid': bool,
})

• Documentação do recurso :

robomimic_mh/can_mh_low_dim

• Tamanho do download : 107.28 MiB

• Tamanho do conjunto de dados : 75.19 MiB

• Cache automático ( documentação ): Sim

• Estrutura de recursos :

FeaturesDict({
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            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
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})

• Documentação do recurso :

robomimic_mh/square_mh_image

• Tamanho do download : 6.48 GiB

• Tamanho do conjunto de dados : 1.07 GiB

• Armazenado em cache automaticamente ( documentação ): Não

• Estrutura de recursos :

FeaturesDict({
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})

• Documentação do recurso :

robomimic_mh/square_mh_low_dim

• Download size : 118.13 MiB

• Dataset size : 80.37 MiB

• Auto-cached ( documentation ): Yes

• Feature structure :

FeaturesDict({
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    '20_percent_train': bool,
    '20_percent_valid': bool,
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            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64),
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    'worse_operator_1': bool,
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    'worse_operator_2': bool,
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    'worse_valid': bool,
})

• Feature documentation :

robomimic_mh/transport_mh_image

• Download size : 31.47 GiB

• Dataset size : 7.69 GiB

• Auto-cached ( documentation ): No

• Feature structure :

FeaturesDict({
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    '20_percent_valid': bool,
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    '50_percent_train': bool,
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    'episode_id': string,
    'horizon': int32,
    'okay': bool,
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    'okay_train': bool,
    'okay_valid': bool,
    'steps': Dataset({
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        '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 :

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 :