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Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.ORCID iD: 0000-0001-6738-9872
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. (RPL/EECS)ORCID iD: 0000-0002-7796-1438
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
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2018 (English)In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018Conference paper, Published paper (Refereed)
Abstract [en]

Mobile robot navigation in complex and dynamic environments is a challenging but important problem. Reinforcement learning approaches fail to solve these tasks efficiently due to reward sparsities, temporal complexities and high-dimensionality of sensorimotor spaces which are inherent in such problems. We present a novel approach to train action policies to acquire navigation skills for wheel-legged robots using deep reinforcement learning. The policy maps height-map image observations to motor commands to navigate to a target position while avoiding obstacles. We propose to acquire the multifaceted navigation skill by learning and exploiting a number of manageable navigation behaviors. We also introduce a domain randomization technique to improve the versatility of the training samples. We demonstrate experimentally a significant improvement in terms of data-efficiency, success rate, robustness against irrelevant sensory data, and also the quality of the maneuver skills.

Place, publisher, year, edition, pages
2018.
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-256310DOI: 10.1109/IROS.2018.8593702Scopus ID: 2-s2.0-85062964303OAI: oai:DiVA.org:kth-256310DiVA, id: diva2:1344495
Conference
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Funder
EU, Horizon 2020, 644839
Note

QC 20190902

Available from: 2019-08-21 Created: 2019-08-21 Last updated: 2019-09-02Bibliographically approved

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Publisher's full textScopushttp://10.1109/IROS.2018.8593702

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Ghadirzadeh, AliFolkesson, JohnJensfelt, Patric

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