Machine learning algorithms, which aim at extracting and recognizing patterns from observed data, will play a central role for enabling robotic systems that efficiently and seamlessly adapt to changing environments. While current supervised learning techniques have been very successful at tasks such as image recognition, speech recognition, or personalized recommendations, their extension to cyber-physical and robotic systems leads to many challenges. A promising approach to deal with these challenges is to incorporate the wealth of a-priori known structure that many robotic and cyber-physical systems have, such as approximate models based on first principles, symmetries, and invariants. This could improve the sample complexity, ensure that the predictions generalize to unseen situations, and could also facilitate down-stream tasks. 

At the Learning and Dynamical Systems Group we build on techniques from machine learning, dynamical systems, and control theory for enabling future cyber-physical and robotic systems. While rigorous theory and mathematical analysis forms the basis of our research, we also evaluate our methods in experiments on real-world systems.

The Learning and Dynamical Systems Group is an independent research group funded by the Emmy Noether Fellowship, the Branco Weiss Fellowship, and an Amazon research grant.

See also our youtube channel: https://www.youtube.com/@lds-group .