Learning to Efficiently Plan Robust Frictional Multi-Object Grasps

2022/10/13 by Agboh, Wisdom C., Sharma, Satvik, Srinivas, Kishore +7 · 1 citation
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Robotics (cs.RO)

paper · doi:10.48550/arxiv.2210.07420

Abstract

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.

Cited by

• Dynamic Buffers: Cost-Efficient Planning for Tabletop Rearrangement with Stacking

Related

• Multi-Object Grasping in the Plane
• Push-MOG: Efficient Pushing to Consolidate Polygonal Objects for Multi-Object Grasping
• Multiple-object Grasping Using a Multiple-suction-cup Vacuum Gripper in Cluttered Scenes
• Multi-Object Grasping -- Generating Efficient Robotic Picking and Transferring Policy
• Multi-Object Grasping -- Estimating the Number of Objects in a Robotic Grasp