3 citations · 3 across the 7 of their papers we have counts for
7 papers
A Decade of Bayesian Optimization for Controller Tuning and Robot Learning: Tutorial, Review, and Future Prospects
David Stenger, Paul Brunzema, Johanna Menn +3
In the past decade, Bayesian optimization (BO) has emerged as a powerful and adaptable framework for automatic controller tuning and robot learning. This article offers a comprehen…
Scalable Gaussian Process Regression via Deterministic Trigonometric Features: Uniform Bounds for Safe Model Predictive Control
Julius Jagdt, Johanna Menn, Sebastian Trimpe +2
Learning-based Model Predictive Control (MPC) using Gaussian processes (GPs) is an effective approach for safe control in the presence of model mismatch. High-probability safety gu…
Local Preferential Bayesian Optimization
Johanna Menn, Miriam Kober, Paul Brunzema +2
Bayesian optimization (BO) is a popular and effective approach for tuning expensive, noisy experiments, but requires the formulation of an explicit objective function. Preferential…
Preferential Bayesian Optimization with Crash Feedback
Johanna Menn, David Stenger, Sebastian Trimpe
Bayesian optimization is a popular black-box optimization method for parameter learning in control and robotics. It typically requires an objective function that reflects the user'…
Safety in safe Bayesian optimization and its ramifications for control
Christian Fiedler, Johanna Menn, Sebastian Trimpe
A recurring and important task in control engineering is parameter tuning under constraints, which conceptually amounts to optimization of a blackbox function accessible only throu…
Lipschitz Safe Bayesian Optimization for Automotive Control
Johanna Menn, Pietro Pelizzari, Michael Fleps-Dezasse +1
Controller tuning is a labor-intensive process that requires human intervention and expert knowledge. Bayesian optimization has been applied successfully in different fields to aut…
On Safety in Safe Bayesian Optimization
Christian Fiedler, Johanna Menn, Lukas Kreisköther +1
Optimizing an unknown function under safety constraints is a central task in robotics, biomedical engineering, and many other disciplines, and increasingly safe Bayesian Optimizati…