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
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.
BibTeX
@inproceedings{iros2023_learningtoeffici,
title = {Learning to Efficiently Plan Robust Frictional Multi-Object Grasps},
author = {Wisdom C. Agboh and Satvik Sharma and Kishore Srinivas and Mallika Parulekar and Gaurav Datta and Tianshuang Qiu and Jeffrey Ichnowski and Eugen Solowjow and Mehmet Dogar and Ken Goldberg},
booktitle = {IROS 2023},
year = {2023}
}