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

Angelini, Franco; Bianchi, Matteo; Garabini, Manolo; Bicchi, Antonio; Della Santina, C.

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

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

Conference paper (2021)

Authors

Franco Angelini University of Pisa, Istituto Italiano di Tecnologia

Matteo Bianchi University of Pisa

Manolo Garabini University of Pisa

Antonio Bicchi University of Pisa, Istituto Italiano di Tecnologia

C. Della Santina Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering , Technische Universität München, Deutsches Zentrum für Luft- und Raumfahrt (DLR)

Research Group

Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)

DOI: https://doi.org/10.1007/978-3-030-64313-3_2

Human-inspired control Motion and motor control Natural machine motion

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Published Date

2021

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

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.

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