A Bayesian Optimization Framework for the Automatic Tuning of MPC-Based Shared Controllers
Anne van der Horst, Bastiaan Guillermo Lorenzo Meere, Dinesh Krishnamoorthy, Saray Bakker, Bram van de Vrande, Henry Stoutjesdijk, Marco Alonso, Elena Torta
Abstract
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.