Workshop on Gaussian Process Learning for Systems and Control 

 One challenge in controller design is to achieve the desired performance and guarantee safe operation, e.g., via the satisfaction of constraints despite the presence of disturbances. One way to deal with uncertainties is obtaining an estimate via machine learning techniques, such as Gaussian processes. Gaussian processes have been used increasingly as a data-driven technique within the past two decades due to many beneficial properties such as the bias-variance trade-off and the close relation to Bayesian mathematics.

In contrast to most of the methods, Gaussian process models provide a regression function and a measure for the uncertainty of the prediction. This powerful property makes them very attractive for many applications in control, e.g., model predictive control, robust control, reinforcement learning, and general optimization tasks, as the uncertainty measure provides convergence, performance, and safety guarantees. However, fusing/embedding machine learning, especially Gaussian processes in a closed-loop control system, poses several challenges, such as closed-loop uncertainty propagation or real-time feasible online learning.

This tutorial-style workshop aims to provide insight into the fundamentals behind Gaussian processes for modeling and control and sketches some of the open challenges and opportunities using Gaussian processes for modeling and control.

Experts/lecturers with experience in Gaussian processes and (optimization-based) control from academia and industry will introduce Gaussian processes’ basics and spotlight Gaussian processes’ opportunities for the control community and recent advances in learning-based control under uncertainties in general. The workshop targets an audience from graduate level to experienced theoretical and practically oriented control engineers who aim to improve their knowledge in controller design under uncertainties leveraging Gaussian processes and machine learning.