Machine learning for advanced control of autonomous robots. We are continuously looking for outstanding students who are eager to do their Master thesis on a challenging project in the area of Learning Control.
The ability to learn will be a key requirement for future robotic systems, which are envisioned to act autonomously in complex and changing environments. A core research area at the Autonomous Motion Department (AMD) is learning for control. We seek to combine techniques from machine learning, control theory, and optimization to develop intelligent control algorithms for the next generation of autonomous robots. 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 at AMD to study various aspects of autonomous robots.
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 optimal control of movement, adaptive and learning control for complex robots, Gaussian process optimization for self-tuning control, learning to manipulate objects, and data-efficient learning of dynamic models.
See the project description and the research overview page for more information.