Three-fold Adaptivity in Groups of Robots: The Effect of Social Learning

Jacqueline Heinerman, Dexter Drupsteen, A.E. Eiben

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Adapting the control systems of robots on the fly is important in robotic systems of the future. In this paper we present and investigate a three-fold adaptive system based on evolution, individual and social learning in a group of robots and report on a proof-of-concept study based on epucks. We distinguish inheritable and learnable components in the robots' makeup, specify and implement operators for evolution, learning and social learning, and test the system in an arena where the task is to learn to avoid obstacles. In particular, we make the sensory layout evolvable, the locomotion control system learnable and investigate the effects of including social learning in the 'adaptation engine'. Our simulation experiments demonstrate that the full mix of three adaptive mechanisms is practicable and that adding social learning leads to better controllers faster.
Original languageEnglish
Title of host publicationGECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference
Place of PublicationMadrid, SP
PublisherAssociation for Computing Machinery, Inc
Pages177-183
Number of pages7
ISBN (Electronic)9781450334723
DOIs
Publication statusPublished - 2015
Event16th Genetic and Evolutionary Computation Conference (GECCO 2015) - Madrid, Spain
Duration: 11 Jul 201515 Jul 2015

Conference

Conference16th Genetic and Evolutionary Computation Conference (GECCO 2015)
CountrySpain
CityMadrid
Period11/07/1515/07/15

Keywords

  • Evolutionary robotics
  • Individual learning
  • Neural networks
  • Obstacle avoidance
  • On-line evolution
  • Social learning

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    Heinerman, J., Drupsteen, D., & Eiben, A. E. (2015). Three-fold Adaptivity in Groups of Robots: The Effect of Social Learning. In GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference (pp. 177-183). Madrid, SP: Association for Computing Machinery, Inc. https://doi.org/10.1145/2739480.2754743