Samenvatting
This paper presents a Bayesian optimization framework for the automatic tuning of shared controllers which are defined as a Model Predictive Control (MPC) problem. The proposed framework includes the design of performance metrics as well as the representation of user inputs for simulation-based optimization. The framework is applied to the optimization of a shared controller for an Image Guided Therapy robot. VR-based user experiments confirm the increase in performance of the automatically tuned MPC shared controller with respect to a hand-tuned baseline version as well as its generalization ability.
Originele taal-2 | Engels |
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Titel | 2024 IEEE International Conference on Robotics and Automation, ICRA 2024 |
Uitgeverij | Institute of Electrical and Electronics Engineers |
Pagina's | 11259-11265 |
Aantal pagina's | 7 |
ISBN van elektronische versie | 979-8-3503-8457-4 |
DOI's | |
Status | Gepubliceerd - 8 aug. 2024 |
Evenement | 2024 IEEE International Conference on Robotics and Automation, ICRA 2024 - Yokohama, Japan Duur: 13 mei 2024 → 17 mei 2024 |
Congres
Congres | 2024 IEEE International Conference on Robotics and Automation, ICRA 2024 |
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Land/Regio | Japan |
Stad | Yokohama |
Periode | 13/05/24 → 17/05/24 |