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*

*Kontaktforfatter

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer 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.

OriginalsprogEngelsk
TitelNeural Information Processing. 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18–22, 2020, Proceedings
RedaktørerHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
Vol/bind5
ForlagSpringer
Publikationsdato2020
Sider695-703
ISBN (Trykt)9783030638221
ISBN (Elektronisk)978-3-030-63823-8
DOI
StatusUdgivet - 2020
Begivenhed27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand
Varighed: 18. nov. 202022. nov. 2020

Konference

Konference27th International Conference on Neural Information Processing, ICONIP 2020
Land/OmrådeThailand
ByBangkok
Periode18/11/202022/11/2020
NavnCommunications in Computer and Information Science
Vol/bind1333
ISSN1865-0929

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