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
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 perform very well for real robots.
BibTeX
@inproceedings{icra2023_cmgnetanendtoend,
title = {CMG-Net: An End-to-End Contact-based Multi-Finger Dexterous Grasping Network},
author = {Mingze Wei and Yaomin Huang and Zhiyuan Xu and Ning Liu and Zhengping Che and Xinyu Zhang and Chaomin Shen and Feifei Feng and Chun Shan and Jian Tang},
booktitle = {ICRA 2023},
year = {2023}
}