DVGG: Deep Variational Grasp Generation for Dextrous Manipulation

doi:10.1109/LRA.2022.3140424

	DVGG: Deep Variational Grasp Generation for Dextrous Manipulation
	Wei, Wei 1,4; Li, Daheng 1,4; Wang, Peng 2,3,4; Li, Yiming 1,4; Li, Wanyi 4; Luo, Yongkang 4; Zhong, Jun 4
刊名	IEEE ROBOTICS AND AUTOMATION LETTERS
	2022-04-01
卷号	7 期号:2 页码:1659-1666
关键词	Deep learning in grasping and manipulation multifingered hands computer vision for automation point cloud completion iterative refinement
ISSN号	2377-3766
DOI	10.1109/LRA.2022.3140424
通讯作者	Wang, Peng(peng_wang@ia.ac.cn)
英文摘要	Grasping with anthropomorphic robotic hands involves much more hand-object interactions compared to parallel-jaw grippers. Modeling hand-object interactions is essential to the study of multi-finger hand dextrous manipulation. This work presents DVGG, an efficient grasp generation network that takes single-view observation as input and predicts high-quality grasp configurations for unknown objects. In general, our generative model consists of three components: 1) Point cloud completion for the target object based on the partial observation; 2) Diverse sets of grasps generation given the complete point cloud; 3) Iterative grasp pose refinement for physically plausible grasp optimization. To train our model, we build a large-scale grasping dataset that contains about 300 common object models with 1.5 M annotated grasps in simulation. Experiments in simulation show that our model can predict robust grasp poses with a wide variety and high success rate. Real robot platform experiments demonstrate that the model trained on our dataset performs well in the real world. Remarkably, our method achieves a grasp success rate of 70.7% for novel objects in the real robot platform, which is a significant improvement over the baseline methods.
资助项目	National Natural Science Foundation of China[91748131] ; National Natural Science Foundation of China[62006229] ; National Natural Science Foundation of China[61771471] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050106]
WOS研究方向	Robotics
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000745810900010
资助机构	National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Science
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/47250]
专题	智能机器人系统研究
通讯作者	Wang, Peng
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China 3.Chinese Acad Sci, Ctr Artificial Intelligence & Robot, Hong Kong Inst Sci & Innovat, Hong Kong 999077, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wei, Wei,Li, Daheng,Wang, Peng,et al. DVGG: Deep Variational Grasp Generation for Dextrous Manipulation[J]. IEEE ROBOTICS AND AUTOMATION LETTERS,2022,7(2):1659-1666.
APA	Wei, Wei.,Li, Daheng.,Wang, Peng.,Li, Yiming.,Li, Wanyi.,...&Zhong, Jun.(2022).DVGG: Deep Variational Grasp Generation for Dextrous Manipulation.IEEE ROBOTICS AND AUTOMATION LETTERS,7(2),1659-1666.
MLA	Wei, Wei,et al."DVGG: Deep Variational Grasp Generation for Dextrous Manipulation".IEEE ROBOTICS AND AUTOMATION LETTERS 7.2(2022):1659-1666.