The Effects of Adaptive Control on Learning Directed Locomotion

Fuda Van Diggelen, Robert Babuska, A. E. Eiben

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

This study is motivated by evolutionary robot systems where robot bodies and brains evolve simultaneously. In such systems robot 'birth' must be followed by 'infant learning' by a learning method that works for various morphologies evolution may produce. Here we address the task of directed locomotion in modular robots with controllers based on Central Pattern Generators. We present a bio-inspired adaptive feedback mechanism that uses a forward model and an inverse model that can be learned on-the-fly. We compare two versions (a simple and a sophisticated one) of this concept to a traditional (open-loop) controller using Bayesian optimization as a learning algorithm. The experimental results show that the sophisticated version outperforms the simple one and the traditional controller. It leads to a better performance and more robust controllers that better cope with noise.

Original languageEnglish
Title of host publicationProceedings of the IEEE Symposium Series on Computational Intelligence, SSCI 2020
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages2117-2124
ISBN (Electronic)978-1-7281-2547-3
DOIs
Publication statusPublished - 2020
Event2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 - Virtual, Canberra, Australia
Duration: 1 Dec 20204 Dec 2020

Conference

Conference2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
CountryAustralia
CityVirtual, Canberra
Period1/12/204/12/20

Keywords

  • Adaptive Control
  • Directed Locomotion
  • Evolutionary Robotics
  • Reality Gap

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