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 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以随机位置和朝向放置在平面上，需要通过单物体抓取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，我们引入摩擦以提高每小时抓取次数。我们利用真实示例训练神经网络来规划鲁棒的多物体抓取。在物理实验中，我们发现，与先前的多物体抓取研究相比，成功率提高了13.7%，每小时抓取次数增加了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增加了3.1倍。