The ability to learn will be a key requirement for future robotic and other intelligent systems, which are envisioned to act autonomously in complex and changing environments. A core research area in the Intelligent Control Systems Group (ICS) is learning for control. We combine techniques from machine learning, control theory, and optimization to develop intelligent control algorithms for the next generation of autonomous systems. In particular, we focus on the special requirements that real-time control systems pose for learning algorithms, such as guarantees for stability, robustness, and efficient computation.
While rigorous theory and mathematical analysis form the basis of our research, we validate our methods in experiments on physical robots. We have a number of state-of-the-art robotic platforms to study various aspects of autonomous systems.
We are continuously looking for outstanding students who are eager to do their Master thesis on a challenging research project in a highly dynamic research environment. We have a variety of possible projects available, ranging from very theoretical to practical, and covering different aspects of learning control and robotics. Examples of possible topics include adaptive and learning control for complex robots, non-parametric learning of dynamic models, model-based reinforcement learning, learning-based model predictive control, and Bayesian optimization.
See the project description and the Intelligent Control Systems Group page for more information.