Dynamical State Forcing on Central Pattern Generators for Efficient Robot Locomotion Control

Thirawat Chuthong, Binggwong Leung, Kawee Tiraborisute, Potiwat Ngamkajornwiwat, Poramate Manoonpong, Nat Dilokthanakul*

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

Abstrakt

Many CPG-based locomotion models have a problem known as the tracking error problem, where the mismatch between the CPG driving signal and the state of the robot can cause undesirable behaviours for legged robots. Towards alleviating this problem, we introduce a mechanism that modulates the CPG signal using the robot’s interoceptive information. The key concept is to generate a driving signal that is easier for the robot to follow, yet can drive the locomotion of the robot. This can be done by nudging the CPG signal in the direction of lower tracking error, which can be analytically calculated. Unlike other reactive CPG, the proposed method does not rely on any parametric learning ability to adjust the shape of the signal, making it a unique option for a biological adaptive motor control. Our experiment results show that the proposed method successfully reduces the tracking error. We also show that the CPG signal, regulated by the proposed method, is robust to perturbation and can smoothly return back to the default pattern.

OriginalsprogEngelsk
TitelNeural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
RedaktørerHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
ForlagSpringer
Publikationsdato2020
Sider799-810
ISBN (Trykt)9783030638320
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
LandThailand
ByBangkok
Periode18/11/202022/11/2020
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind12533 LNCS
ISSN0302-9743

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