CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Network
2023
academicJournal
Zugriff:
In this paper, we propose a novel representation for grasping using contacts between multi-finger robotic hands and objects to be manipulated. This representation significantly reduces the prediction dimensions and accelerates the learning process. We present an effective end-to-end network, CMG-Net, for grasping unknown objects in a cluttered environment by efficiently predicting multi-finger grasp poses and hand configurations from a single-shot point cloud. Moreover, we create a synthetic grasp dataset that consists of five thousand cluttered scenes, 80 object categories, and 20 million annotations. We perform a comprehensive empirical study and demonstrate the effectiveness of our grasping representation and CMG-Net. Our work significantly outperforms the state-of-the-art for three-finger robotic hands. We also demonstrate that the model trained using synthetic data performs very well for real robots. ; Comment: The first two authors are with equal contributions. Paper accepted by ICRA 2023
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CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Network
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Autor/in / Beteiligte Person: | Wei, Mingze ; Huang, Yaomin ; Xu, Zhiyuan ; Liu, Ning ; Che, Zhengping ; Zhang, Xinyu ; Shen, Chaomin ; Feng, Feifei ; Shan, Chun ; Tang, Jian |
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Veröffentlichung: | 2023 |
Medientyp: | academicJournal |
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