  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.
