Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint
This addresses the challenge of generating valid grasping poses for robotics applications, though it is an incremental improvement over existing methods.
The paper tackles the problem of 3D grasp synthesis by introducing an intermediate contact map variable to constrain grasp generation, improving efficiency and generality. It outperforms state-of-the-art methods on two public datasets with various metrics.
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.