ऑस्टिन_सिरियस_डेटासेट_कन्वर्टेड_एक्सटर्नली_टू_आरएलडीएस

• विवरण :

फ्रेंका टेबलटॉप हेरफेर कार्य

• मुखपृष्ठ : https://ut-austin-rpl.github.io/sirius/

• स्रोत कोड : tfds.robotics.rtx.AustinSiriusDatasetConvertedExternallyToRlds

• संस्करण :

  • 0.1.0 (डिफ़ॉल्ट): प्रारंभिक रिलीज़।

• डाउनलोड आकार : Unknown size

• डेटासेट का आकार : 6.55 GiB

• ऑटो-कैश्ड ( दस्तावेज़ीकरण ): नहीं

• विभाजन :

• फ़ीचर संरचना :

FeaturesDict({
    'episode_metadata': FeaturesDict({
        'file_path': Text(shape=(), dtype=string),
    }),
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float32, description=Robot action, consists of [3x ee relative pos, 3x ee relative rotation, 1x gripper action].),
        'action_mode': Tensor(shape=(1,), dtype=float32, description=Type of interaction. -1: initial human demonstration. 1: intervention. 0: autonomuos robot execution (includes pre-intervention class)),
        'discount': Scalar(shape=(), dtype=float32, description=Discount if provided, default to 1.),
        'intv_label': Tensor(shape=(1,), dtype=float32, description=Same as action_modes, except 15 timesteps preceding intervention are labeled as -10.),
        '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({
            'image': Image(shape=(84, 84, 3), dtype=uint8, description=Main camera RGB observation.),
            'state': Tensor(shape=(8,), dtype=float32, description=Default robot state, consists of [7x robot joint state, 1x gripper state].),
            'state_ee': Tensor(shape=(16,), dtype=float32, description=End-effector state, represented as 4x4 homogeneous transformation matrix of ee pose.),
            'state_gripper': Tensor(shape=(1,), dtype=float32, description=Robot gripper opening width. Ranges between ~0 (closed) to ~0.077 (open)),
            'state_joint': Tensor(shape=(7,), dtype=float32, description=Robot 7-dof joint information.),
            'wrist_image': Image(shape=(84, 84, 3), dtype=uint8, description=Wrist camera RGB observation.),
        }),
        '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 ):

• उद्धरण :

@inproceedings{liu2022robot,
    title = {Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment},
    author = {Huihan Liu and Soroush Nasiriany and Lance Zhang and Zhiyao Bao and Yuke Zhu},
    booktitle = {Robotics: Science and Systems (RSS)},
    year = {2023}
}