Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint

• URL: http://arxiv.org/abs/2210.09245v3
• Date: Sat, 6 May 2023 07:53:13 GMT
• Title: Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint
• Authors: Haoming Li, Xinzhuo Lin, Yang Zhou, Xiang Li, Yuchi Huo, Jiming Chen and Qi Ye
• Abstract summary: 3D grasp synthesis generates grasping poses given an input object. We introduce an intermediate variable for grasp contact areas to constrain the grasp generation. Our method outperforms state-of-the-art methods regarding grasp generation on various metrics.
• Score: 18.201389966034263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D grasp synthesis generates grasping poses given an input object. Existing works tackle the problem by learning a direct mapping from objects to the distributions of grasping poses. However, because the physical contact is sensitive to small changes in pose, the high-nonlinear mapping between 3D object representation to valid poses is considerably non-smooth, leading to poor generation efficiency and restricted generality. To tackle the challenge, we introduce an intermediate variable for grasp contact areas to constrain the grasp generation; in other words, we factorize the mapping into two sequential stages by assuming that grasping poses are fully constrained given contact maps: 1) we first learn contact map distributions to generate the potential contact maps for grasps; 2) then learn a mapping from the contact maps to the grasping poses. Further, we propose a penetration-aware optimization with the generated contacts as a consistency constraint for grasp refinement. Extensive validations on two public datasets show that our method outperforms state-of-the-art methods regarding grasp generation on various metrics.

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