Modern technology allows us to collect, process, and share more data than ever before. This data revolution opens up new ways to design control and learning algorithms, which will form the algorithmic foundation for future intelligent systems that shall act autonomously in the physical world. Starting from a discussion of the special challenges when combining machine learning and control, I will present some of our recent research in this exciting area. Using the example of the Apollo robot learning to balance a stick in its hand, I will explain how intelligent agents can learn new behavior from just a few experimental trails. I will also discuss the need for theoretical guarantees in learning-based control, and how we can obtain them by combining learning and control theory.
Biography: Sebastian Trimpe is a Max Planck and Cyber Valley Research Group Leader at the Max Planck Institute (MPI) for Intelligent Systems in Stuttgart, Germany. Sebastian leads the independent Max Planck Research Group on Intelligent Control Systems, which focusses on fundamental research at the intersection of control, machine learning, networks, and robotics. Sebastian obtained his Ph.D. degree in 2013 from ETH Zurich with Raffaello DíAndrea at the Institute for Dynamic Systems and Control. Before, he received a B.Sc. degree in General Engineering Science in 2005, a M.Sc. degree (Dipl.-Ing.) in Electrical Engineering in 2007, and an MBA degree in Technology Management in 2007, all from Hamburg University of Technology. In 2007, he was a research scholar at the University of California at Berkeley. Sebastian is recipient of the General Engineering Award for the best undergraduate degree (2005), a scholarship from the German Academic National Foundation (2002-2007), the triennial IFAC World Congress Interactive Paper Prize (2011), and the Klaus Tschira Award for achievements in public understanding of science (2014).