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Policy transfer in mobile robots using neuro-evolutionary navigation

Authors:

Matt Knudson,

Kagan TumerAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

Pages 1411 - 1412

https://doi.org/10.1145/2330784.2330958

Published: 07 July 2012 Publication History

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Abstract

In this paper, we first present a state/action representation that allows robots to learn good navigation policies, but also allows them to transfer the policy to new and more complex situations. In particular, we show how the evolved policies can transfer to situations with: (i) new tasks (different obstacle and target configurations and densities); and (ii) new sets of sensors (different resolution). Our results show that in all cases, policies evolved in simple environments and transferred to more complex situations outperform policies directly evolved in the complex situation both in terms of overall performance (up to 30%) and convergence speed (up to 90%).

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M. Knudson and K. Tumer. Adaptive navigation for autonomous robots. Robotics and Autonomous Systems, 59:410--420, June 2011.

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Cited By

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Tuyls KStone P(2018)Multiagent Learning ParadigmsMulti-Agent Systems and Agreement Technologies10.1007/978-3-030-01713-2_1(3-21)Online publication date: 14-Oct-2018
https://doi.org/10.1007/978-3-030-01713-2_1
Didi SNitschke G(2016)Multi-agent Behavior-Based Policy TransferApplications of Evolutionary Computation10.1007/978-3-319-31153-1_13(181-197)Online publication date: 2-Apr-2016
https://doi.org/10.1007/978-3-319-31153-1_13

Index Terms

Policy transfer in mobile robots using neuro-evolutionary navigation
1. Computing methodologies
  1. Machine learning

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Published In

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

July 2012

1586 pages

ISBN:9781450311786

DOI:10.1145/2330784

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

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Association for Computing Machinery

New York, NY, United States

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Published: 07 July 2012

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SIGEVO

GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Tuyls KStone P(2018)Multiagent Learning ParadigmsMulti-Agent Systems and Agreement Technologies10.1007/978-3-030-01713-2_1(3-21)Online publication date: 14-Oct-2018
https://doi.org/10.1007/978-3-030-01713-2_1
Didi SNitschke G(2016)Multi-agent Behavior-Based Policy TransferApplications of Evolutionary Computation10.1007/978-3-319-31153-1_13(181-197)Online publication date: 2-Apr-2016
https://doi.org/10.1007/978-3-319-31153-1_13

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Navigation of non-holonomic mobile robot using neuro-fuzzy logic with integrated safe boundary algorithm

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Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system

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Navigation of non-holonomic mobile robot using neuro-fuzzy logic with integrated safe boundary algorithm

Adaptive navigation for autonomous robots

Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system

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