Lukas P. Fröhlich — accepted AI-conference papers

2022

Contextual Tuning of Model Predictive Control for Autonomous Racing

Lukas P. Fröhlich, Christian Küttel, Elena Arcari, Lukas Hewing, Melanie N. Zeilinger, Andrea Carron

IROS 2022poster

Learning-based model predictive control has been widely applied in autonomous racing to improve the closed-loop behaviour of vehicles in a data-driven manner. When environmental conditions change, e.g., due to rain, often only the predictive model is adapted, but the controller parameters are kept c…

Cited by 27 Source ↗Scholar ↗

2020

Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization

Michael Volpp, Lukas P. Fröhlich, Kirsten Fischer, Andreas Doerr, Stefan Falkner, Frank Hutter +1

ICLR 2020spotlight

Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the field of global black-box optimization. Readily available algorithms are typically designed to be universal optimizers and, therefore, often suboptimal for specific tasks. We propose a novel trans…

Cited by 100 Source ↗Scholar ↗

2019

Bayesian Optimization for Policy Search in High-Dimensional Systems via Automatic Domain Selection

Lukas P. Fröhlich, Edgar D. Klenske, Christian G. Daniel, Melanie N. Zeilinger

IROS 2019poster

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 and parameter optimization. However, scaling BO to problems with large input dimensions (>10) remains an open challenge.…

Cited by 12 Source ↗Scholar ↗