स्टैनफोर्ड_कुका_मल्टीमॉडल_डेटासेट_रूपांतरित_बाहरी_से_आरएलडीएस

• विवरण :

बल प्रतिक्रिया के साथ कुका इवा खूंटी सम्मिलन

• मुखपृष्ठ : https://sites.google.com/view/visionandtouch

• स्रोत कोड : tfds.robotics.rtx.StanfordKukaMultimodalDatasetConvertedExternallyToRlds

• संस्करण :

  • 0.1.0 (डिफ़ॉल्ट): प्रारंभिक रिलीज़।

• डाउनलोड आकार : Unknown size

• डेटासेट का आकार : 31.98 GiB

• ऑटो-कैश्ड ( दस्तावेज़ीकरण ): नहीं

• विभाजन :

• फ़ीचर संरचना :

FeaturesDict({
    'episode_metadata': FeaturesDict({
    }),
    'steps': Dataset({
        'action': Tensor(shape=(4,), dtype=float32, description=Robot action, consists of [3x EEF position, 1x gripper open/close].),
        'discount': Scalar(shape=(), dtype=float32, description=Discount if provided, default to 1.),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'language_embedding': Tensor(shape=(512,), dtype=float32, description=Kona language embedding. See https://tfhub.dev/google/universal-sentence-encoder-large/5),
        'language_instruction': Text(shape=(), dtype=string),
        'observation': FeaturesDict({
            'contact': Tensor(shape=(50,), dtype=float32, description=Robot contact information.),
            'depth_image': Tensor(shape=(128, 128, 1), dtype=float32, description=Main depth camera observation.),
            'ee_forces_continuous': Tensor(shape=(50, 6), dtype=float32, description=Robot end-effector forces.),
            'ee_orientation': Tensor(shape=(4,), dtype=float32, description=Robot end-effector orientation quaternion.),
            'ee_orientation_vel': Tensor(shape=(3,), dtype=float32, description=Robot end-effector orientation velocity.),
            'ee_position': Tensor(shape=(3,), dtype=float32, description=Robot end-effector position.),
            'ee_vel': Tensor(shape=(3,), dtype=float32, description=Robot end-effector velocity.),
            'ee_yaw': Tensor(shape=(4,), dtype=float32, description=Robot end-effector yaw.),
            'ee_yaw_delta': Tensor(shape=(4,), dtype=float32, description=Robot end-effector yaw delta.),
            'image': Image(shape=(128, 128, 3), dtype=uint8, description=Main camera RGB observation.),
            'joint_pos': Tensor(shape=(7,), dtype=float32, description=Robot joint positions.),
            'joint_vel': Tensor(shape=(7,), dtype=float32, description=Robot joint velocities.),
            'optical_flow': Tensor(shape=(128, 128, 2), dtype=float32, description=Optical flow.),
            'state': Tensor(shape=(8,), dtype=float32, description=Robot proprioceptive information, [7x joint pos, 1x gripper open/close].),
        }),
        'reward': Scalar(shape=(), dtype=float32, description=Reward if provided, 1 on final step for demos.),
    }),
})

• फ़ीचर दस्तावेज़ीकरण :

• पर्यवेक्षित कुंजियाँ ( as_supervised doc देखें): None

• चित्र ( tfds.show_examples ): समर्थित नहीं है।

• उदाहरण ( tfds.as_dataframe ): गुम है।

• उद्धरण :

@inproceedings{lee2019icra,
  title={Making sense of vision and touch: Self-supervised learning of multimodal representations for contact-rich tasks},
  author={Lee, Michelle A and Zhu, Yuke and Srinivasan, Krishnan and Shah, Parth and Savarese, Silvio and Fei-Fei, Li and  Garg, Animesh and Bohg, Jeannette},
  booktitle={2019 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2019},
  url={https://arxiv.org/abs/1810.10191}
}