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Conference Paper: An Actor-critic Approach For Legible Robot Motion Planner

TitleAn Actor-critic Approach For Legible Robot Motion Planner
Authors
KeywordsTask analysis
Robot motion
Robot kinematics
Biological neural networks
Trajectory
Issue Date2020
PublisherIEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000639
Citation
Proceedings of IEEE International Conference on Robotics and Automation (ICRA 2020), Virtually Conference, 31 May - 31 August 2020, p. 5949-5955 How to Cite?
AbstractIn human-robot collaboration, it is crucial for the robot to make its intentions clear and predictable to the human partners. Inspired by the mutual learning and adaptation of human partners, we suggest an actor-critic approach for a legible robot motion planner. This approach includes two neural networks and a legibility evaluator: 1) A policy network based on deep reinforcement learning (DRL); 2) A Recurrent Neural Networks (RNNs) based sequence to sequence (Seq2Seq) model as a motion predictor; 3) A legibility evaluator that maps motion to legible reward. Through a series of human-subject experiments, we demonstrate that with a simple handicraft function and no real-human data, our method lead to improved collaborative performance against a baseline method and a non-prediction method.
Persistent Identifierhttp://hdl.handle.net/10722/285100
ISSN

 

DC FieldValueLanguage
dc.contributor.authorZhao, X-
dc.contributor.authorFan, T-
dc.contributor.authorWang, D-
dc.contributor.authorHu, Z-
dc.contributor.authorHan, T-
dc.contributor.authorPan, J-
dc.date.accessioned2020-08-07T09:06:45Z-
dc.date.available2020-08-07T09:06:45Z-
dc.date.issued2020-
dc.identifier.citationProceedings of IEEE International Conference on Robotics and Automation (ICRA 2020), Virtually Conference, 31 May - 31 August 2020, p. 5949-5955-
dc.identifier.issn1050-4729-
dc.identifier.urihttp://hdl.handle.net/10722/285100-
dc.description.abstractIn human-robot collaboration, it is crucial for the robot to make its intentions clear and predictable to the human partners. Inspired by the mutual learning and adaptation of human partners, we suggest an actor-critic approach for a legible robot motion planner. This approach includes two neural networks and a legibility evaluator: 1) A policy network based on deep reinforcement learning (DRL); 2) A Recurrent Neural Networks (RNNs) based sequence to sequence (Seq2Seq) model as a motion predictor; 3) A legibility evaluator that maps motion to legible reward. Through a series of human-subject experiments, we demonstrate that with a simple handicraft function and no real-human data, our method lead to improved collaborative performance against a baseline method and a non-prediction method.-
dc.languageeng-
dc.publisherIEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000639-
dc.relation.ispartofIEEE International Conference on Robotics and Automation (ICRA)-
dc.rightsIEEE International Conference on Robotics and Automation (ICRA). Copyright © IEEE, Computer Society.-
dc.rights©2020 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.-
dc.subjectTask analysis-
dc.subjectRobot motion-
dc.subjectRobot kinematics-
dc.subjectBiological neural networks-
dc.subjectTrajectory-
dc.titleAn Actor-critic Approach For Legible Robot Motion Planner-
dc.typeConference_Paper-
dc.identifier.emailPan, J: jpan@cs.hku.hk-
dc.identifier.authorityPan, J=rp01984-
dc.identifier.doi10.1109/ICRA40945.2020.9197102-
dc.identifier.scopuseid_2-s2.0-85092743921-
dc.identifier.hkuros312262-
dc.identifier.spage5949-
dc.identifier.epage5955-
dc.publisher.placeUnited States-

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