Understanding object affordance can help in designing better and more robust robotic grasping. Existing work in the computer vision community formulates the object affordance understanding as a grasping pose generation problem, which treats the problem as a black box by learning a mapping between objects and the distributions of possible grasping poses for the objects. On the other hand, in the robotics community, estimating object affordance represented by contact maps is of the most importance as localizing the positions of the possible affordance can help the planning of grasping actions. In this paper, we propose to formulate the object affordance understanding as both contacts and grasp poses generation. we factorize the learning task into two sequential stages, rather than the black-box strategy: (1) we first reason the contact maps by allowing multi-modal contact generation; (2) assuming that grasping poses are fully constrained given contact maps, we learn a one-to-one mapping from the contact maps to the grasping poses. Further, we propose a penetration-aware partial optimization from the intermediate contacts. It combines local and global optimization for the refinement of the partial poses of the generated grasps exhibiting penetration. Extensive validations on two public datasets show our method outperforms state-of-the-art methods regarding grasp generation on various metrics.

本篇研究通过介绍一种新的中间变量来提高三维抓取合成的效率，以解决现有方法想要直接从物体映射到抓取姿势的非光滑和低效问题，并提出了一种基于接触区域的因子图形式来描述抓取姿态生成过程，并通过实验验证了优化方法的有效性。

Contact2Grasp: 基于手物接触限制的三维握取合成