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Learning to Efficiently Plan Robust Frictional Multi-Object Grasps by Agboh, Wisdom C.; Sharma, Satvik; Srinivas, Kishore; Parulekar, Mallika; Datta, Gaurav; Qiu, Tianshuang; Ichnowski, Jeffrey; Solowjow, Eugen; Dogar, Mehmet; Goldberg, Ken is a scholarly article available to read on EtoBox.

What is Learning to Efficiently Plan Robust Frictional Multi-Object Grasps about?

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.

Author

Agboh, Wisdom C.; Sharma, Satvik; Srinivas, Kishore; Parulekar, Mallika; Datta, Gaurav; Qiu, Tianshuang; Ichnowski, Jeffrey; Solowjow, Eugen; Dogar, Mehmet; Goldberg, Ken

Published

2022

Language

EN