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.

翻译：3D抓取合成针对输入物体生成抓取姿态。现有工作通过学习从物体到抓取姿态分布的直接映射来解决该问题。然而，由于物理接触对姿态的微小变化高度敏感，从三维物体表征到有效姿态的高度非线性映射存在显著非光滑性，导致生成效率低下且泛化性受限。为应对这一挑战，我们引入抓取接触区域作为中间变量以约束抓取生成；换言之，通过假设给定接触图即可完全约束抓取姿态，我们将映射分解为两个连续阶段：1）首先学习接触图分布以生成潜在抓取接触图；2）随后学习从接触图到抓取姿态的映射。进一步，我们提出一种穿透感知优化方法，将生成的接触作为抓取优化的一致性约束。在两个公开数据集上的广泛验证表明，本方法在多种指标上的抓取生成性能均优于现有最先进方法。