Adaptive Neural Control for Efficient Rhythmic Movement Generation and Online Frequency Adaptation of a Compliant Robot Arm

Florentijn Degroote, Mathias Thor, Jevgeni Ignasov, Jørgen Christian Larsen, Emilia Motoasca, Poramate Manoonpong*

*Corresponding author for this work

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

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Abstract

In this paper, we propose an adaptive and simple neural control approach for a robot arm with soft/compliant materials, called GummiArm. The control approach is based on a minimal two-neuron oscillator network (acting as a central pattern generator) and an error-based dual integral learning (DIL) method for efficient rhythmic movement generation and frequency adaptation, respectively. By using this approach, we can precisely generate rhythmic motion for GummiArm and allow it to quickly adapt its motion to handle physical and environmental changes as well as interacting with a human safely. Experimental results for GummiArm in different scenarios (e.g., dealing with different joint stiffnesses, working against elastic loads, and interacting with a human) are provided to illustrate the effectiveness of the proposed adaptive neural control approach.

Original languageEnglish
Title of host publicationNeural Information Processing. 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18–22, 2020, Proceedings
EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
Volume5
PublisherSpringer
Publication date2020
Pages695-703
ISBN (Print)9783030638221
ISBN (Electronic)978-3-030-63823-8
DOIs
Publication statusPublished - 2020
Event27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand
Duration: 18. Nov 202022. Nov 2020

Conference

Conference27th International Conference on Neural Information Processing, ICONIP 2020
Country/TerritoryThailand
CityBangkok
Period18/11/202022/11/2020
SeriesCommunications in Computer and Information Science
Volume1333
ISSN1865-0929

Keywords

  • Adaptive robot behavior
  • Artificial intelligence
  • Human-machine interaction
  • Soft robot

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