  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.
