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arXiv · 2210.07420

Learning to Efficiently Plan Robust Frictional Multi-Object Grasps

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

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Wisdom C. Agboh, Satvik Sharma, Kishore Srinivas, Mallika Parulekar, Gaurav Datta, Tianshuang Qiu, Jeffrey Ichnowski, Eugen Solowjow, Mehmet Dogar, Ken Goldberg. 2022-10-13. Learning to Efficiently Plan Robust Frictional Multi-Object Grasps. https://arxiv.org/abs/2210.07420

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