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 an 11.7% increase in success rates, a 1.7x
increase in picks per hour, and an 8.2x decrease in grasp planning time
compared to prior work on multi-object grasping. Videos are available at
this https URL.

通过实验数据，证明使用考虑摩擦力的神经网络在多物体抓取问题中可以显著提高成功率、提高拿取效率、降低规划时间

高效学习规划稳健摩擦多物体抓握

Learning to Efficiently Plan Robust Frictional Multi-Object Grasps

We consider a novel problem where multiple rigid convex polygonal objects
rest in randomly placed positions and orientations on a planar surface visible
from an overhead camera. The objective is to efficiently grasp and transport
all objects into a bin using multi-object push-grasps, where multiple objects
are pushed together to facilitate multi-object grasping. We provide necessary
conditions for frictionless multi-object push-grasps and apply these to filter
inadmissible grasps in a novel multi-object grasp planner. We find that our
planner is 19 times faster than a Mujoco simulator baseline. We also propose a
picking algorithm that uses both single- and multi-object grasps to pick
objects. In physical grasping experiments comparing performance with a
single-object picking baseline, we find that the frictionless multi-object
grasping system achieves 13.6\% higher grasp success and is 59.9\% faster, from
212 PPH to 340 PPH. See
https://sites.google.com/view/multi-object-grasping for videos and code.

本文研究了使用摄像头识别平面上的多个刚体凸多边形物体，利用多目标推动 - 抓取算法将它们可靠地移动到垃圾桶里。实验结果表明，使用摩擦力的多目标抓取系统比单目标抓取系统表现更好，速度大约快了 59.9％。