Michael Lutter: Inductive Biases for Learning Robot Control


In order to leave the factory floors and research labs, future robots must abandon their stiff and pre-programmed movements and be capable to learn complex policies. These control policies must be adaptive to the inevitable changes of the physical world and must only select feasible actions. Current black-box function approximation approaches are not sufficient for such tasks as these approaches overfit to the training domain and commonly perform damaging actions. In this talk, I want to introduce my research focusing on robot learning for physical robots by using inductive biases and deep learning. This approach combines the representational power of deep networks and directly includes the feasibility and robustness as inductive bias within the learning problem to enable the application to the physical system. Using this approach we showed that deep networks can be constrained to learn physically plausible models and optimal feedback controller by incorporating domain knowledge and demonstrated the applicability to the physical system.

Feb 20, 2020 3:00 PM — 4:00 PM
SML Seminar
AI Centre, UCL


Michael Lutter is a Ph.D. student at the TU Darmstadt Institute for Intelligent Autonomous Systems (IAS). His research focuses on combining domain knowledge and deep learning to enable learning of complex robot controllers that are feasible for the physical system. Prior to this Michael held a researcher position at the Technical University of Munich (TUM). At TUM, he worked for the Human Brain Project and taught various lectures within the Elite Master Program Neuroengineering. His educational background covers a Bachelors in Engineering Management from University of Duisburg Essen and a Masters in Electrical Engineering from the Technical University of Munich. During his undergraduate studies he also spent one semester at the Massachusetts Institute of Technology (MIT) studying electrical engineering and computer science. Besides his studies, Michael worked with ThyssenKrupp, Siemens, ABB, General Electric and NVIDIA and received multiple scholarships for academic excellence.
Marc Deisenroth
DeepMind Chair in Artificial Intelligence