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：三维抓取合成旨在针对给定物体生成抓取姿态。现有方法通过学习从物体到抓取姿态分布的直接映射来处理该问题。然而，由于物理接触对姿态的微小变化高度敏感，三维物体表征到有效姿态之间的高非线性映射存在显著的非平滑性，导致生成效率低下且泛化能力受限。为应对这一挑战，我们引入抓取接触区域作为中间变量来约束抓取生成；换言之，通过假设抓取姿态在给定接触图时完全受约束，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。此外，我们提出一种基于穿透感知的优化方法，以生成的接触作为一致性约束进行抓取细化。在两个公共数据集上的广泛验证表明，我们的方法在多种评估指标上均优于现有最优方法。