Ensuring safety and achieving human-level driving performance remain challenges for
autonomous vehicles, especially in safety-critical situations. As a key component of artificial …

In robotics, optimizing controller parameters under safety constraints is an important
challenge. Safe Bayesian optimization (BO) quantifies uncertainty in the objective and …

This paper presents a data-driven strategy to streamline the deployment of model-based
controllers in legged robotic hardware platforms. Our approach leverages a model-free safe …

In numerous applications across all science and engineering areas, there are optimization
problems where both the objective function and the constraints have no closed-form …

Learning optimal control policies directly on physical systems is challenging. Even a single
failure can lead to costly hardware damage. Most existing model-free learning methods that …

Changing conditions or environments can cause system dynamics to vary over time. To
ensure optimal control performance, controllers should adapt to these changes. When the …

In recent years, the use of Bayesian optimization (BO) for efficient automatic tuning of
general controller structures through iterative closed-loop experiments, has been attracting …

Controller tuning and parameter optimization are crucial in system design to improve both
the controller and underlying system performance. Bayesian optimization (BO) has been …

Controller tuning based on black-box optimization allows to automatically tune performance-
critical parameters wrt mostly arbitrary high-level closed-loop control objectives. However, a …

Controller tuning is a labor-intensive process that requires human intervention and expert
knowledge. Bayesian optimization has been applied successfully in different fields to …