Reservoir-based Online Adaptive Forward Models with Neural Control for Complex Locomotion in a Hexapod Robot

Poramate Manoonpong, Sakyasingha Dasgupta, Dennis Goldschmidt, Florentin Wörgötter

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

Abstract

Walking animals show fascinating locomotor abilities and complex behaviors. Biological study has revealed that such complex behaviors is a result of a combination of biomechanics and neural mechanisms. While biomechanics allows for flexibility and a variety of movements, neural mechanisms generate locomotion, make predictions, and provide adaptation. Inspired by this finding, we present here an artificial bio-inspired walking system which combines biomechanics (in terms of its body and leg structures) and neural mechanisms. The neural mechanisms consist of 1) central pattern generator-based control for generating basic rhythmic patterns and coordinated movements, 2) reservoir-based adaptive forward models with efference copies for sensory prediction as well as state estimation, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Simulation results show that this bio-inspired approach allows the walking robot to perform complex locomotor abilities including walking on undulated terrains, crossing a large gap, as well as climbing over a high obstacle and a fleet of stairs.
Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks (IJCNN)
Number of pages8
PublisherIEEE
Publication date2014
Pages3295 - 3302
ISBN (Print)978-1-4799-6627-1
DOIs
Publication statusPublished - 2014
EventThe International Joint Conference on Neural Networks - Beijing, China
Duration: 6. Jul 201411. Jul 2014

Conference

ConferenceThe International Joint Conference on Neural Networks
Country/TerritoryChina
CityBeijing
Period06/07/201411/07/2014
SeriesInternational Conference on Neural Networks. Proceedings
ISSN1098-7576

Keywords

  • Forward models
  • Walking robots
  • Neural control

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