Bejjani, W, Dogar, MR orcid.org/0000-0002-6896-5461 and Leonetti, M orcid.org/0000-0002-3831-2400 (2020) Learning Physics-Based Manipulation in Clutter: Combining Image-Based Generalization and Look-Ahead Planning. In: Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019). IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), 03-08 Nov 2019, Macau, China. IEEE , pp. 6562-6569. ISBN 978-1-7281-4004-9
Abstract
Physics-based manipulation in clutter involves complex interaction between multiple objects. In this paper, we consider the problem of learning, from interaction in a physics simulator, manipulation skills to solve this multi-step sequential decision making problem in the real world. Our approach has two key properties: (i) the ability to generalize and transfer manipulation skills (over the type, shape, and number of objects in the scene) using an abstract image-based representation that enables a neural network to learn useful features; and (ii) the ability to perform look-ahead planning in the image space using a physics simulator, which is essential for such multi-step problems. We show, in sets of simulated and real-world experiments (video available on https://youtu.be/EmkUQfyvwkY), that by learning to evaluate actions in an abstract image-based representation of the real world, the robot can generalize and adapt to the object shapes in challenging real-world environments.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
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/R031193/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Jul 2019 15:04 |
Last Modified: | 28 Mar 2020 19:41 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/IROS40897.2019.8967717 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:149114 |