Hasan, M orcid.org/0000-0002-7477-7133, Warburton, M orcid.org/0000-0001-5309-4424, Agboh, WC orcid.org/0000-0002-0242-0215 et al. (6 more authors) (2020) Human-like Planning for Reaching in Cluttered Environments. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). ICRA 2020, 31 May - 04 Jun 2020, Paris, France. IEEE , pp. 7784-7790. ISBN 978-1-7281-7396-2
Abstract
Humans, in comparison to robots, are remarkably adept at reaching for objects in cluttered environments. The best existing robot planners are based on random sampling of configuration space- which becomes excessively high-dimensional with large number of objects. Consequently, most planners often fail to efficiently find object manipulation plans in such environments. We addressed this problem by identifying high-level manipulation plans in humans, and transferring these skills to robot planners. We used virtual reality to capture human participants reaching for a target object on a tabletop cluttered with obstacles. From this, we devised a qualitative representation of the task space to abstract the decision making, irrespective of the number of obstacles. Based on this representation, human demonstrations were segmented and used to train decision classifiers. Using these classifiers, our planner produced a list of waypoints in task space. These waypoints provided a high-level plan, which could be transferred to an arbitrary robot model and used to initialise a local trajectory optimiser. We evaluated this approach through testing on unseen human VR data, a physics-based robot simulation, and a real robot (dataset and code are publicly available 1 ). We found that the human-like planner outperformed a state-of-the-art standard trajectory optimisation algorithm, and was able to generate effective strategies for rapid planning- irrespective of the number of obstacles in the environment.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020, IEEE. This is an author produced version of a paper accepted for publication in 2020 International Conference on Robotics and Automation (ICRA). 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. |
Keywords: | Task analysis; Planning; Robots; Testing; Feature extraction; Trajectory; Standards |
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) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Psychology (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/R031193/1 EU - European Union 825619 Alan Turing Institute No ref given |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Mar 2020 13:59 |
Last Modified: | 19 Dec 2024 15:55 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICRA40945.2020.9196665 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:158051 |