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Conference Paper: Learning Resilient Behaviors for Navigation Under Uncertainty

TitleLearning Resilient Behaviors for Navigation Under Uncertainty
Authors
KeywordsUncertainty
Navigation
Robots
Task analysis
Training
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, Paris, France, 31 May - 31 August 2020, p. 5299-5305 How to Cite?
AbstractDeep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically. However, the underlying neural network polices have not been widely deployed in real-world applications, especially in these safety-critical tasks (e.g., autonomous driving). One of the reasons is that the learned policy cannot perform flexible and resilient behaviors as traditional methods to adapt to diverse environments. In this paper, we consider the problem that a mobile robot learns adaptive and resilient behaviors for navigating in unseen uncertain environments while avoiding collisions. We present a novel approach for uncertainty-aware navigation by introducing an uncertaintyaware predictor to model the environmental uncertainty, and we propose a novel uncertainty-aware navigation network to learn resilient behaviors in the prior unknown environments. To train the proposed uncertainty-aware network more stably and efficiently, we present the temperature decay training paradigm, which balances exploration and exploitation during the training process. Our experimental evaluation demonstrates that our approach can learn resilient behaviors in diverse environments and generate adaptive trajectories according to environmental uncertainties.
Persistent Identifierhttp://hdl.handle.net/10722/284628
ISSN

 

DC FieldValueLanguage
dc.contributor.authorFan, T-
dc.contributor.authorLong, P-
dc.contributor.authorLiu, W-
dc.contributor.authorPan, J-
dc.contributor.authorYang, RG-
dc.contributor.authorManocha, D-
dc.date.accessioned2020-08-07T09:00:22Z-
dc.date.available2020-08-07T09:00:22Z-
dc.date.issued2020-
dc.identifier.citationProceedings of IEEE International Conference on Robotics and Automation (ICRA 2020), Virtually Conference, Paris, France, 31 May - 31 August 2020, p. 5299-5305-
dc.identifier.issn1050-4729-
dc.identifier.urihttp://hdl.handle.net/10722/284628-
dc.description.abstractDeep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically. However, the underlying neural network polices have not been widely deployed in real-world applications, especially in these safety-critical tasks (e.g., autonomous driving). One of the reasons is that the learned policy cannot perform flexible and resilient behaviors as traditional methods to adapt to diverse environments. In this paper, we consider the problem that a mobile robot learns adaptive and resilient behaviors for navigating in unseen uncertain environments while avoiding collisions. We present a novel approach for uncertainty-aware navigation by introducing an uncertaintyaware predictor to model the environmental uncertainty, and we propose a novel uncertainty-aware navigation network to learn resilient behaviors in the prior unknown environments. To train the proposed uncertainty-aware network more stably and efficiently, we present the temperature decay training paradigm, which balances exploration and exploitation during the training process. Our experimental evaluation demonstrates that our approach can learn resilient behaviors in diverse environments and generate adaptive trajectories according to environmental uncertainties.-
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.subjectUncertainty-
dc.subjectNavigation-
dc.subjectRobots-
dc.subjectTask analysis-
dc.subjectTraining-
dc.titleLearning Resilient Behaviors for Navigation Under Uncertainty-
dc.typeConference_Paper-
dc.identifier.emailPan, J: jpan@cs.hku.hk-
dc.identifier.authorityPan, J=rp01984-
dc.identifier.doi10.1109/ICRA40945.2020.9196785-
dc.identifier.scopuseid_2-s2.0-85092689946-
dc.identifier.hkuros312152-
dc.identifier.spage5299-
dc.identifier.epage5305-
dc.publisher.placeUnited States-

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