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arXiv · 2303.13182

CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Network

Mingze Wei·Yaomin Huang·Zhiyuan Xu·Ning Liu·Zhengping Che·Xinyu Zhang·Chaomin Shen·Feifei Feng·Chun Shan·Jian Tang
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Abstract

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.

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Mingze Wei, Yaomin Huang, Zhiyuan Xu, Ning Liu, Zhengping Che, Xinyu Zhang, Chaomin Shen, Feifei Feng, Chun Shan, Jian Tang. 2023-03-23. CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Network. https://arxiv.org/abs/2303.13182

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