571 citations · 1.3k across the 123 of their papers we have counts for
10 papers · 1 filter
Cautious Bayesian Optimization for Efficient and Scalable Policy Search
Lukas P. Fröhlich, Melanie N. Zeilinger, Edgar D. Klenske
Sample efficiency is one of the key factors when applying policy search to real-world problems. In recent years, Bayesian Optimization (BO) has become prominent in the field of rob…
Volume Control of Low-Cost Ventilator with Automatic Set-Point Adaptation
Lukas Hewing, Marcel Menner, Nikolaos Tachatos +6
This paper considers the control design for a low-cost ventilator that is based on a manual resuscitator bag (also known as AmbuBag) to pump air into the lungs of a patient who is…
Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations
Elena Arcari, Andrea Carron, Melanie N. Zeilinger
Data availability has dramatically increased in recent years, driving model-based control methods to exploit learning techniques for improving the system description, and thus cont…
Distributed Safe Learning using an Invariance-based Safety Framework
Andrea Carron, Jerome Sieber, Melanie N. Zeilinger
In large-scale networks of uncertain dynamical systems, where communication is limited and there is a strong interaction among subsystems, learning local models and control policie…
Bayesian model predictive control: Efficient model exploration and regret bounds using posterior sampling
Kim P. Wabersich, Melanie N. Zeilinger
Tight performance specifications in combination with operational constraints make model predictive control (MPC) the method of choice in various industries. As the performance of a…
Cautious Bayesian MPC: Regret Analysis and Bounds on the Number of Unsafe Learning Episodes
Kim P. Wabersich, Melanie N. Zeilinger
This paper investigates the combination of model predictive control (MPC) concepts and posterior sampling techniques and proposes a simple constraint tightening technique to introd…
Maximum Likelihood Methods for Inverse Learning of Optimal Controllers
Marcel Menner, Melanie N. Zeilinger
This paper presents a framework for inverse learning of objective functions for constrained optimal control problems, which is based on the Karush-Kuhn-Tucker (KKT) conditions. We…
Data-Driven Distributed Stochastic Model Predictive Control with Closed-Loop Chance Constraint Satisfaction
Simon Muntwiler, Kim P. Wabersich, Lukas Hewing +1
Distributed model predictive control methods for uncertain systems often suffer from considerable conservatism and can tolerate only small uncertainties due to the use of robust fo…
Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization
Lukas P. Fröhlich, Edgar D. Klenske, Julia Vinogradska +2
We consider the problem of robust optimization within the well-established Bayesian optimization (BO) framework. While BO is intrinsically robust to noisy evaluations of the object…
Bayesian Optimization for Policy Search in High-Dimensional Systems via Automatic Domain Selection
Lukas P. Fröhlich, Edgar D. Klenske, Christian G. Daniel +1
Bayesian Optimization (BO) is an effective method for optimizing expensive-to-evaluate black-box functions with a wide range of applications for example in robotics, system design…