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.

翻译：本文提出了一种基于贝叶斯优化的框架，用于自动调优以模型预测控制（MPC）问题形式定义的共享控制器。该框架包含性能指标的设计以及基于仿真的优化中用户输入的表示方法。我们将此框架应用于图像引导治疗机器人共享控制器的优化。基于虚拟现实的用户实验证实，相较于手动调优的基线版本，自动调优后的MPC共享控制器在性能上有所提升，并展现出良好的泛化能力。