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

Franco Angelini, Matteo Bianchi, Manolo Garabini, Antonio Bicchi, Cosimo Della Santina

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

1 Zitat (Scopus)

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.

OriginalspracheEnglisch
TitelBiomimetic and Biohybrid Systems - 9th International Conference, Living Machines 2020, Proceedings
Redakteure/-innenVasiliki Vouloutsi, Anna Mura, Paul F. M. J. Verschure, Falk Tauber, Thomas Speck, Tony J. Prescott
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Seiten12-16
Seitenumfang5
ISBN (Print)9783030643126
DOIs
PublikationsstatusVeröffentlicht - 2021
Veranstaltung9th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2020 - Virtual, Online
Dauer: 28 Juli 201930 Juli 2019

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band12413 LNAI
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz9th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2020
OrtVirtual, Online
Zeitraum28/07/1930/07/19

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