I am a Ph.D. student at the Intelligent Control Systems Group. My research interests lie at the intersection of control theory, machine learning, and non-linear dynamics. In particular, I am interested in understanding how structure inherent in physical systems and control tasks can be leveraged to improve learning algorithms and enable knowledge transfer.
I obtained a master's degree in Theoretical Mechanical Engineering at TU Hamburg. During my masters, I worked on the combination of neural networks and sliding control for end-to-end vehicle maneuvering under Professor J. Karl Hedrick at the University of California, Berkeley. Further, I developed localization and control methods for the 'HippoCampus' AUV project. My master's thesis evolved around the combination of stochastic optimal control and Gaussian processes for autonomous field exploration.
In 2nd Annual Conference on Learning for Dynamics and Control, June 2020 (inproceedings) Accepted
The identification of the constrained dynamics of mechanical systems is often challenging. Learning methods promise to ease an analytical analysis, but require considerable amounts of data for training. We propose to combine insights from analytical mechanics with Gaussian process regression to improve the model's data efficiency and constraint integrity. The result is a Gaussian process model that incorporates a priori constraint knowledge such that its predictions adhere to Gauss' principle of least constraint. In return, predictions of the system's acceleration naturally respect potentially non-ideal (non-)holonomic equality constraints. As corollary results, our model enables to infer the acceleration of the unconstrained system from data of the constrained system and enables knowledge transfer between differing constraint configurations.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems