articleIEEE Transactions on RoboticsJun 28, 2023Closed access

AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains

HFHao-Shu FangCWChenxi WangHFHongjie FangMGMinghao GouJLJirong Liu

Shanghai Jiao Tong University · Beijing Academy of Artificial Intelligence · +1 more institution

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Abstract

As the basis for prehensile manipulation, it is vital to enable robots to grasp as robustly as humans. Our innate grasping system is prompt, accurate, flexible, and continuous across spatial and temporal domains. Few existing methods cover all these properties for robot grasping. In this article, we propose AnyGrasp for grasp perception to enable robots these abilities using a parallel gripper. Specifically, we develop a dense supervision strategy with real perception and analytic labels in the spatial–temporal domain. Additional awareness of objects' center-of-mass is incorporated into the learning process to help improve grasping stability. Utilization of grasp correspondence across observations enables…

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Topics & keywords

Topics
Keywords
  • GRASP
  • Artificial intelligence
  • Robot
  • Computer science
  • Computer vision
  • Perception
  • Process (computing)
  • Noise (video)
UN Sustainable Development Goals
  • Life below water
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