Systems Seminar - ECE
The science of autonomy: A happy symbiosis among learning, control and physics.
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In this talk I will present an information theoretic approach to stochastic optimal control that has advantages over classical methodologies and theories for decision making under uncertainty. The main idea is that there are certain connections between optimality principles in control and
information theoretic inequalities in statistical physics that allow us to solve hard decision making problems in robotics, autonomous systems and beyond. There are essentially two different points of view of the same "thing" and these two different points of view overlap for a fairly general class of dynamical systems that undergo stochastic effects. I will also present a holistic view to autonomy that collapses planning, perception and control into one computational engine, and ask questions related to how organization and structure affects functionality and performance in "engineered" organisms. Finally, I will provide alternative frameworks for uncertainty representation and suggest ways to incorporate these representations within decision making and control.
Evangelos A. Theodorou is an assistant professor with the Guggenheim School of aerospace engineering at Georgia Institute of Technology. He is also affiliated with the Institute of Robotics and Intelligent Machines. Evangelos Theodorou earned his Diploma in Electronic and Computer Engineering from the Technical University of Crete (TUC), Greece in 2001. He has also received a MSc in Production Engineering from TUC in 2003, a MSc in Computer Science and Engineering from University of Minnesota in spring of 2007 and a MSc in Electrical Engineering on dynamics and controls from the University of Southern California (USC) in Spring 2010. In May of 2011 he graduated with his PhD, in Computer Science at USC. After his PhD, he was a Postdoctoral Research Fello with the department of computer science and engineering, University of Washington, Seattle. Evangelos Theodorou is the recipient of the King-Sun Fu best paper award of the IEEE Transactions on Robotics for the year 2012 and recipient of the best paper award in cognitive robotics in International Conference of Robotics and Automation 2011. He was also the finalist for the best paper award in International Conference of Humanoid Robotics 2010 and International Conference of Robotics and Automation 2017. His theoretical research spans the areas of stochastic optimal control theory, machine learning, information theory and statistical physics. Applications involve learning, planning and control in autonomous, robotics and aerospace systems.