Control under uncertainty is an omnipresent problem in robotics that typically arises when robots must cope with unknown environments/tasks. Robot control typically ignores uncertainty by considering only the expected outcomes of the robot’s internal model. Interestingly, neuroscientist have shown that humans adapt their decisions depending on the level of uncertainty which is not reflected in the expected values, but in higher order statistics. In this talk I will first present an approach to systematically address this problem in the context of stochastic optimal control. I will then give an example of how the robot’s internal model structure defines the level uncertainty and its distribution. Finally, experiments in a physical human-robot interaction setting will illustrate the capabilities of this approach.
Biography: Jose R. Medina received his degree on Ingeniero Superior en Informática from the University of Seville, Spain, in 2008, and his Ph.D. degree at the Chair of Information-Oriented Control, Department of Electrical Engineering and Information Technology, Technical University of Munich (TUM), Germany, in 2015. Since January 2016 he is a postdoctoral researcher at the Learning Algorithms and Systems Laboratory, École Polytechnique Fédérale de Lausanne (EPFL). His research interests include machine learning and robot control with applications in physical human-robot interaction.