Iterative learning control as a framework for human-inspired control with bio-mimetic actuators
Franco Angelini (University of Pisa, Istituto Italiano di Tecnologia)
Matteo Bianchi (University of Pisa)
Manolo Garabini (University of Pisa)
Antonio Bicchi (Istituto Italiano di Tecnologia, University of Pisa)
Cosimo Della Santina (Technische Universität München, Deutsches Zentrum für Luft- und Raumfahrt (DLR), TU Delft - Mechanical Engineering)
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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.