Agboh, W.C., Sharma, S., Srinivas, K. et al. (7 more authors) (2023) Learning to Efficiently Plan Robust Frictional Multi-Object Grasps. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 01-05 Oct 2023, Detroit, USA. IEEE , pp. 10660-10667. ISBN 978-1-6654-9191-4
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author produced version of a conference paper accepted for publication in Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/V052659/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Aug 2023 09:36 |
Last Modified: | 18 Jan 2024 11:41 |
Published Version: | https://ieeexplore.ieee.org/document/10341895 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/IROS55552.2023.10341895 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202110 |