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.
翻译:另一方面,在机器人群体中,估计接触地图所代表物体是否具有可捕捉力有助于设计更好、更稳健的机器人捕捉能力。在本文件中,我们提议将对象是否具有可捕捉力的理解作为接触和捉拿力的生成两个相继阶段。我们将学习任务分为两个相继阶段,而不是黑盒战略:1)我们首先通过多式接触生成来解释接触地图;2)假设接触地图所显示的捉拿取力完全受限制,我们从接触地图中学习一对一的绘图,到握取力。此外,我们提议从中间接触中进行渗透-觉部分优化。我们提议将目标是否具有可捕捉力的理解作为接触和握握力的生成组合。我们把学习任务分为两个相继阶段,而不是黑盒战略:1)我们首先通过允许多式接触生成接触方式来解释接触地图;2)假设接触地图所代表物体是否完全受限制,我们从接触地图中学习一对一对一的绘图。我们提议从中间接触中进行渗透式部分优化。我们提议将地方和全球的优化结合,以完善所生成的握握手的部分配置,以展示渗透。关于两种公共数据生成方法的广泛验证。