CPG Driven RBF Network Control with Reinforcement Learning for Gait Optimization of a Dung Beetle-Like Robot

Matheshwaran Pitchai*, Xiaofeng Xiong, Mathias Thor, Peter Billeschou, Peter Lukas Mailänder, Binggwong Leung, Tomas Kulvicius, Poramate Manoonpong

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In this paper, we employ a central pattern generator (CPG) driven radial basis function network (RBFN) based controller to learn optimized locomotion for a complex dung beetle-like robot using reinforcement learning approach called “Policy Improvement with Path Integrals (PI $$^2$$ )”. Our CPG driven RBFN controller is inspired by rhythmic dynamic movement primitives (DMPs). The controller can be also seen as an extension to a traditional CPG controller, which usually controls only the frequency of the motor patterns but not the shape. Our controller uses the CPG to control the frequency while the RBFN takes care of the shape of the motor patterns. In this paper, we only focus on the shape of the motor patterns and optimize those with respect to walking speed and energy efficiency. As a result, the robot can travel faster and consume less power than using only the CPG controller.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2019 : Theoretical Neural Computation - 28th International Conference on Artificial Neural Networks, 2019, Proceedings
EditorsIgor V. Tetko, Vera Kurková, Pavel Karpov, Fabian Theis
VolumePart 1
PublisherSpringer
Publication date2019
Pages698-710
ISBN (Print)9783030304867
ISBN (Electronic)978-3-030-30487-4
DOIs
Publication statusPublished - 2019
Event28th International Conference on Artificial Neural Networks, ICANN 2019 - Munich, Germany
Duration: 17. Sept 201919. Sept 2019

Conference

Conference28th International Conference on Artificial Neural Networks, ICANN 2019
Country/TerritoryGermany
CityMunich
Period17/09/201919/09/2019
SeriesLecture Notes in Computer Science
Volume11727 LNCS
ISSN0302-9743

Keywords

  • Artificial neural networks
  • Brain inspired computing
  • Reinforcement learning

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