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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Acknowledgments.
This project has been supported by European Union’s Horizon 2020 research and innovation programme under grant agreement 780883 (THING) and 871237 (Sophia), by ERC Synergy Grant 810346 (Natural BionicS) and by the Italian Ministry of Education and Research (MIUR) in the framework of the CrossLab project (Departments of Excellence).
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Angelini, F., Bianchi, M., Garabini, M., Bicchi, A., Santina, C.D. (2020). Iterative Learning Control as a Framework for Human-Inspired Control with Bio-mimetic Actuators. In: Vouloutsi, V., Mura, A., Tauber, F., Speck, T., Prescott, T.J., Verschure, P.F.M.J. (eds) Biomimetic and Biohybrid Systems. Living Machines 2020. Lecture Notes in Computer Science(), vol 12413. Springer, Cham. https://doi.org/10.1007/978-3-030-64313-3_2
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DOI: https://doi.org/10.1007/978-3-030-64313-3_2
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