Learning to Efficiently Plan Robust Frictional Multi-Object Grasps

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.

I. Introduction

When skilled waiters clear tables, they grasp multiple utensils and dishes in a single motion. Similarly, it is inefficient for robotic picking systems in warehouses and fulfillment centers to only handle a single object at a time. Picking multiple objects at once can significantly increase picks per hour (PPH), the total number of objects picked from a scene in an hour. In prior work on multi-object grasping [1], PPH was increased compared to single-object picking. This improvement was limited due to a frictionless grasping assumption and no considerations of robustness. In this work, we find that considering friction and quickly generating robust grasps can lead to significant improvements in PPH. For example, grasps like those shown in Fig. 1 cannot exist without appropriate friction between objects. An important question that then arises is how to generate such robust frictional grasps.