vima_converted_externally_to_rlds

• Descrição :

Conjunto de dados SIM de um único braço robótico executando tarefas de mesa geradas processualmente com prompts multimodais, mais de 600 mil trajetórias

• Página inicial : https://vimalabs.github.io/

• Código fonte : tfds.robotics.rtx.VimaConvertedExternallyToRlds

• Versões :

  • 0.1.0 (padrão): versão inicial.

• Tamanho do download : Unknown size

• Tamanho do conjunto de dados : Unknown size

• Armazenado em cache automaticamente ( documentação ): Desconhecido

• Divisões :

• Estrutura de recursos :

FeaturesDict({
    'episode_metadata': FeaturesDict({
        'action_bounds': FeaturesDict({
            'high': Tensor(shape=(3,), dtype=float32),
            'low': Tensor(shape=(3,), dtype=float32),
        }),
        'end-effector type': string,
        'failure': Scalar(shape=(), dtype=bool),
        'file_path': string,
        'n_objects': Scalar(shape=(), dtype=int64),
        'num_steps': Scalar(shape=(), dtype=int64),
        'robot_components_seg_ids': Sequence(Scalar(shape=(), dtype=int64)),
        'seed': Scalar(shape=(), dtype=int64),
        'success': Scalar(shape=(), dtype=bool),
        'task': string,
    }),
    'steps': Dataset({
        'action': FeaturesDict({
            'pose0_position': Tensor(shape=(3,), dtype=float32),
            'pose0_rotation': Tensor(shape=(4,), dtype=float32),
            'pose1_position': Tensor(shape=(3,), dtype=float32),
            'pose1_rotation': Tensor(shape=(4,), dtype=float32),
        }),
        'discount': Scalar(shape=(), dtype=float32),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'multimodal_instruction': string,
        'multimodal_instruction_assets': FeaturesDict({
            'asset_type': Sequence(string),
            'frontal_image': Sequence(Tensor(shape=(128, 256, 3), dtype=uint8)),
            'frontal_segmentation': Sequence(Tensor(shape=(128, 256), dtype=uint8)),
            'image': Sequence(Tensor(shape=(128, 256, 3), dtype=uint8)),
            'key_name': Sequence(string),
            'segmentation': Sequence(Tensor(shape=(128, 256), dtype=uint8)),
            'segmentation_obj_info': Sequence({
                'obj_name': Sequence(string),
                'segm_id': Sequence(Scalar(shape=(), dtype=int64)),
                'texture_name': Sequence(string),
            }),
        }),
        'observation': FeaturesDict({
            'ee': int64,
            'frontal_image': Tensor(shape=(128, 256, 3), dtype=uint8),
            'frontal_segmentation': Tensor(shape=(128, 256), dtype=uint8),
            'image': Tensor(shape=(128, 256, 3), dtype=uint8),
            'segmentation': Tensor(shape=(128, 256), dtype=uint8),
            'segmentation_obj_info': FeaturesDict({
                'obj_name': Sequence(string),
                'segm_id': Sequence(Scalar(shape=(), dtype=int64)),
                'texture_name': Sequence(string),
            }),
        }),
        'reward': Scalar(shape=(), dtype=float32),
    }),
})

• Documentação de recursos :

• Chaves supervisionadas (consulte o documento as_supervised ): None

• Figura ( tfds.show_examples ): Não suportado.

• Exemplos ( tfds.as_dataframe ): Ausente.

• Citação :

@inproceedings{jiang2023vima,  title     = {VIMA: General Robot Manipulation with Multimodal Prompts},  author    = {Yunfan Jiang and Agrim Gupta and Zichen Zhang and Guanzhi Wang and Yongqiang Dou and Yanjun Chen and Li Fei-Fei and Anima Anandkumar and Yuke Zhu and Linxi Fan}, booktitle = {Fortieth International Conference on Machine Learning},  year      = {2023}. }