We consider a decluttering problem where multiple rigid convex polygonal objects rest in randomly placed positions and orientations on a planar surface and must be efficiently transported to a packing box using both single and multi-object grasps. Prior work considered frictionless multi-object grasping. In this paper, we introduce friction to increase the number of potential grasps for a given group of objects, and thus increase picks per hour. We train a neural network using real examples to plan robust multi-object grasps. In physical experiments, we find a 13.7% increase in success rate, a 1.6x increase in picks per hour, and a 6.3x decrease in grasp planning time compared to prior work on multi-object grasping. Compared to single-object grasping, we find a 3.1x increase in picks per hour.

翻译：我们考虑一个整理问题，其中多个刚性凸多边形物体随机放置在一个平面上的位置和方向，需要利用单物体和多物体抓取高效地运输到包装箱中。先前的研究考虑了无摩擦的多物体抓取。在本文中，我们引入摩擦以增加给定物体组中潜在抓取的数量，从而提高每小时抓取次数。我们使用真实示例训练了一个神经网络来规划稳健的多物体抓取。在物理实验中，与先前多物体抓取研究相比，我们发现成功率提高了13.7%，每小时抓取次数提高了1.6倍，抓取规划时间减少了6.3倍。与单物体抓取相比，我们发现每小时抓取次数提高了3.1倍。