论文标题
学会有效地计划强大的摩擦多对象格拉斯普
Learning to Efficiently Plan Robust Frictional Multi-Object Grasps
论文作者
论文摘要
我们考虑了一个整洁的问题，其中多个刚性凸多边形对象位于平面表面上随机放置的位置和方向，必须有效地使用单个和多对象的grasps将其有效地运输到包装盒中。先前的工作认为无摩擦多物体抓握。在本文中，我们引入摩擦以增加给定的对象的潜在掌握次数，从而增加每小时的选择。我们使用真实示例来训练神经网络，以计划强大的多对象掌握。在物理实验中，与先前的多对象掌握工作相比，成功率增加了13.7％，每小时提取1.6倍增加1.6倍，抓紧计划时间减少了6.3倍。与单对象握把相比，我们发现每小时的选拔量增加了3.1倍。
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.
