Iterative learning control as a framework for human-inspired control with bio-mimetic actuators

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2 Scopus citations

Abstract

The synergy between musculoskeletal and central nervous systems empowers humans to achieve a high level of motor performance, which is still unmatched in bio-inspired robotic systems. Literature already presents a wide range of robots that mimic the human body. However, under a control point of view, substantial advancements are still needed to fully exploit the new possibilities provided by these systems. In this paper, we test experimentally that an Iterative Learning Control algorithm can be used to reproduce functionalities of the human central nervous system - i.e. learning by repetition, after-effect on known trajectories and anticipatory behavior - while controlling a bio-mimetically actuated robotic arm.

Original languageEnglish
Title of host publicationBiomimetic and Biohybrid Systems - 9th International Conference, Living Machines 2020, Proceedings
EditorsVasiliki Vouloutsi, Anna Mura, Paul F. M. J. Verschure, Falk Tauber, Thomas Speck, Tony J. Prescott
PublisherSpringer Science and Business Media Deutschland GmbH
Pages12-16
Number of pages5
ISBN (Print)9783030643126
DOIs
StatePublished - 2021
Event9th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2020 - Virtual, Online
Duration: 28 Jul 201930 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12413 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2020
CityVirtual, Online
Period28/07/1930/07/19

Keywords

  • Human-inspired control
  • Motion and motor control
  • Natural machine motion

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