Abstract: The recent advances in robot learning have been largely fueled by model-free deep reinforcement learning algorithms. These black-box methods utilize large datasets and deep networks to discover good behaviors. The existing knowledge of robotics and control is ignored and only the information contained within the data is leveraged. In this talk we want to take a different approach and evaluate the combination of knowledge and data-driven learning. We show that this combination enables sample-efficient learning for physical robots and that generic knowledge from physics and control can be incorporated in deep network representations. The use of inductive biases for robot learning yields robots that learn dynamic tasks within minutes and robust control policies for under-actuated systems.

Bio: Michael Lutter joined the Institute for Intelligent Autonomous Systems (IAS) at TU Darmstadt in July 2017. Prior to this Michael held a researcher position at the Technical University of Munich (TUM) for bio-inspired learning for robotics. During this time he worked on the Human Brain Project, a European H2020 FET flagship project. In addition to his studies, Michael worked for ThyssenKrupp, Siemens and General Electric and received multiple scholarships for academic excellence and his current research.