Christopher König — accepted AI-conference papers

2023

Safe Risk-Averse Bayesian Optimization for Controller Tuning

Christopher König, Miks Ozols, Anastasia Makarova, Efe C. Balta, Andreas Krause, Alisa Rupenyan

RA-L 2023

Controller tuning and parameter optimization are crucial in system design to improve both the controller and underlying system performance. Bayesian optimization (BO) has been established as an efficient model-free method for controller tuning and adaptation. Standard methods, however, are not enoug

Cited by 13 Source ↗Scholar ↗

2021

Safe and Efficient Model-free Adaptive Control via Bayesian Optimization

Christopher König, Matteo Turchetta, John Lygeros, Alisa Rupenyan, Andreas Krause

ICRA 2021poster

Adaptive control approaches yield high-performance controllers when a precise system model or suitable parametrizations of the controller are available. Existing data-driven approaches for adaptive control mostly augment standard model-based methods with additional information about uncertainties in…

Cited by 52 Source ↗Scholar ↗