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

doi:10.1007/978-3-030-64313-3_2

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

^*Corresponding author for this work

Learning & Autonomous Control

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

50 Downloads (Pure)

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 language	English
Title of host publication	Biomimetic and Biohybrid Systems
Subtitle of host publication	Proceedings of the 9th International Conference, Living Machines 2020
Editors	Vasiliki Vouloutsi, Anna Mura, Paul F. M. J. Verschure, Falk Tauber, Thomas Speck, Tony J. Prescott
Publisher	Springer
Pages	12-16
ISBN (Electronic)	978-3-030-64313-3
ISBN (Print)	978-3-030-64312-6
DOIs	https://doi.org/10.1007/978-3-030-64313-3_2
Publication status	Published - 2021
Event	9th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2020 - Virtual, Online Duration: 28 Jul 2019 → 30 Jul 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12413 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2020
City	Virtual, Online
Period	28/07/19 → 30/07/19

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Human-inspired control
Motion and motor control
Natural machine motion

Access to Document

10.1007/978-3-030-64313-3_2

Angelini2020_Chapter_IterativeLearningControlAsAFraFinal published version, 774 KB

Cite this

Angelini, F., Bianchi, M., Garabini, M., Bicchi, A., & Santina, C. D. (2021). Iterative learning control as a framework for human-inspired control with bio-mimetic actuators. In V. Vouloutsi, A. Mura, P. F. M. J. Verschure, F. Tauber, T. Speck, & T. J. Prescott (Eds.), Biomimetic and Biohybrid Systems : Proceedings of the 9th International Conference, Living Machines 2020 (pp. 12-16). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12413 LNAI). Springer. https://doi.org/10.1007/978-3-030-64313-3_2

Angelini, Franco ; Bianchi, Matteo ; Garabini, Manolo et al. / Iterative learning control as a framework for human-inspired control with bio-mimetic actuators. Biomimetic and Biohybrid Systems : Proceedings of the 9th International Conference, Living Machines 2020. editor / Vasiliki Vouloutsi ; Anna Mura ; Paul F. M. J. Verschure ; Falk Tauber ; Thomas Speck ; Tony J. Prescott. Springer, 2021. pp. 12-16 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e30b45f6f8b04129b0d27bd0e0418033,

title = "Iterative learning control as a framework for human-inspired control with bio-mimetic actuators",

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.",

keywords = "Human-inspired control, Motion and motor control, Natural machine motion",

author = "Franco Angelini and Matteo Bianchi and Manolo Garabini and Antonio Bicchi and Santina, {Cosimo Della}",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ; 9th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2020 ; Conference date: 28-07-2019 Through 30-07-2019",

year = "2021",

doi = "10.1007/978-3-030-64313-3_2",

language = "English",

isbn = "978-3-030-64312-6",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "12--16",

editor = "Vasiliki Vouloutsi and Anna Mura and Verschure, {Paul F. M. J.} and Falk Tauber and Thomas Speck and Prescott, {Tony J.}",

booktitle = "Biomimetic and Biohybrid Systems",

}

Angelini, F, Bianchi, M, Garabini, M, Bicchi, A & Santina, CD 2021, Iterative learning control as a framework for human-inspired control with bio-mimetic actuators. in V Vouloutsi, A Mura, PFMJ Verschure, F Tauber, T Speck & TJ Prescott (eds), Biomimetic and Biohybrid Systems : Proceedings of the 9th International Conference, Living Machines 2020. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12413 LNAI, Springer, pp. 12-16, 9th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2020, Virtual, Online, 28/07/19. https://doi.org/10.1007/978-3-030-64313-3_2

Iterative learning control as a framework for human-inspired control with bio-mimetic actuators. / Angelini, Franco; Bianchi, Matteo; Garabini, Manolo et al.
Biomimetic and Biohybrid Systems : Proceedings of the 9th International Conference, Living Machines 2020. ed. / Vasiliki Vouloutsi; Anna Mura; Paul F. M. J. Verschure; Falk Tauber; Thomas Speck; Tony J. Prescott. Springer, 2021. p. 12-16 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12413 LNAI).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Iterative learning control as a framework for human-inspired control with bio-mimetic actuators

AU - Angelini, Franco

AU - Bianchi, Matteo

AU - Garabini, Manolo

AU - Bicchi, Antonio

AU - Santina, Cosimo Della

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Human-inspired control

KW - Motion and motor control

KW - Natural machine motion

UR - http://www.scopus.com/inward/record.url?scp=85107297658&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-64313-3_2

DO - 10.1007/978-3-030-64313-3_2

M3 - Conference contribution

AN - SCOPUS:85107297658

SN - 978-3-030-64312-6

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 12

EP - 16

BT - Biomimetic and Biohybrid Systems

A2 - Vouloutsi, Vasiliki

A2 - Mura, Anna

A2 - Verschure, Paul F. M. J.

A2 - Tauber, Falk

A2 - Speck, Thomas

A2 - Prescott, Tony J.

PB - Springer

T2 - 9th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2020

Y2 - 28 July 2019 through 30 July 2019

ER -

Angelini F, Bianchi M, Garabini M, Bicchi A, Santina CD. Iterative learning control as a framework for human-inspired control with bio-mimetic actuators. In Vouloutsi V, Mura A, Verschure PFMJ, Tauber F, Speck T, Prescott TJ, editors, Biomimetic and Biohybrid Systems : Proceedings of the 9th International Conference, Living Machines 2020. Springer. 2021. p. 12-16. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-64313-3_2

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this