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Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation
Department of Electronic and Computer Engineering, Department of Mechanical and Aerospace Engineering, Hong Kong University of Science and Technology, Hong Kong.
Department of Mechanical Engineering and Material Science, Yale University, New Haven Connecticut, USA.
Department of Electronic and Computer Engineering, Department of Mechanical and Aerospace Engineering, Hong Kong University of Science and Technology, Hong Kong.
Centre for Autonomous Systems, EECS, KTH Royal Institute of Technology, Stockholm, Sweden.
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2019 (English)In: 2019 International Conference on Robotics and Automation (ICRA) / [ed] Howard, A; Althoefer, K; Arai, F; Arrichiello, F; Caputo, B; Castellanos, J; Hauser, K; Isler, V; Kim, J; Liu, H; Oh, P; Santos, V; Scaramuzza, D; Ude, A; Voyles, R; Yamane, K; Okamura, A;, IEEE , 2019, p. 2153-2160Conference paper, Published paper (Refereed)
Abstract [en]

Moving a human body or a large and bulky object may require the strength of whole arm manipulation (WAM). This type of manipulation places the load on the robot's arms and relies on global properties of the interaction to succeed-rather than local contacts such as grasping or non-prehensile pushing. In this paper, we learn to generate motions that enable WAM for holding and transporting of humans in certain rescue or patient care scenarios. We model the task as a reinforcement learning problem in order to provide a robot behavior that can directly respond to external perturbation and human motion. For this, we represent global properties of the robot-human interaction with topology-based coordinates that are computed from arm and torso positions. These coordinates also allow transferring the learned policy to other body shapes and sizes. For training and evaluation, we simulate a dynamic sea rescue scenario and show in quantitative experiments that the policy can solve unseen scenarios with differently-shaped humans, floating humans, or with perception noise. Our qualitative experiments show the subsequent transporting after holding is achieved and we demonstrate that the policy can be directly transferred to a real world setting.

Place, publisher, year, edition, pages
IEEE , 2019. p. 2153-2160
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729, E-ISSN 2577-087X
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-78531DOI: 10.1109/ICRA.2019.8794160ISI: 000494942301099Scopus ID: 2-s2.0-85068443674ISBN: 978-1-5386-6026-3 (print)ISBN: 978-1-5386-6027-0 (electronic)OAI: oai:DiVA.org:oru-78531DiVA, id: diva2:1376819
Conference
International Conference on Robotics and Automation (ICRA), Montreal, Canada, May 20-24, 2019
Funder
Knut and Alice Wallenberg FoundationSwedish Foundation for Strategic Research
Note

Funding Agencies:

HKUST SSTSP project RoMRO  FP802

HKUST IGN project  IGN16EG09

HKUST PGS Fund of Office of Vice-President (Research & Graduate Studies) 

Available from: 2019-12-10 Created: 2019-12-10 Last updated: 2019-12-10Bibliographically approved

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Stork, Johannes Andreas

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