Virtual-to-real Deep Reinforcement Learning : Continuous Control of Mobile Robots for Mapless Navigation

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Author(s)

  • Lei Tai
  • Giuseppe Paolo
  • Ming Liu

Detail(s)

Original languageEnglish
Title of host publication2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Pages31-36
ISBN (Electronic)9781538626825
Publication statusPublished - Dec 2017

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Title2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017)
LocationVancouver Convention Centre
PlaceCanada
CityVancouver, BC
Period24 - 28 September 2017

Abstract

We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners for mobile ground robots with a laser range sensor mostly depend on the obstacle map of the navigation environment where both the highly precise laser sensor and the obstacle map building work of the environment are indispensable. We show that, through an asynchronous deep reinforcement learning method, a mapless motion planner can be trained end-to-end without any manually designed features and prior demonstrations. The trained planner can be directly applied in unseen virtual and real environments. The experiments show that the proposed mapless motion planner can navigate the nonholonomic mobile robot to the desired targets without colliding with any obstacles.

Citation Format(s)

Virtual-to-real Deep Reinforcement Learning : Continuous Control of Mobile Robots for Mapless Navigation. / Tai, Lei; Paolo, Giuseppe; Liu, Ming.

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017. p. 31-36 8202134 (IEEE International Conference on Intelligent Robots and Systems).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)