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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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This is a part of the GT MAP activities on Control. GT MAP is a place for research discussion and collaboration. We welcome participation of any researcher interested in discussing his/her project and exchange ideas with Mathematicians.
There will be light refreshments through out the event. This seminar will be held in Skiles 006 and refreshments at Skiles Atrium.
A couple of members of Prof. Theodorou's group will present their research
3:00 PM - 3:45PM Prof. Theodorou will give a talk on " The science of autonomy: A "happy" symbiosis between learning, control and physics."
3:45PM -- 4:00PM Break with Discussions
4:00PM - 4:25PM another talk.
4:25PM - 5PM Discussion of open problems stemming from the presentations.
Title: The science of autonomy: A "happy" symbiosis between learning, control and physics.
Abstract: In this talk I will present an information theoretic approach to stochastic optimal control and inference 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 of autonomy that collapses planning, perception and control into one computational engine, and ask questions such as how organization and structure relates to computation and performance. The last part of my talk includes computational frameworks for uncertainty representation and suggests ways to incorporate these representations within decision making and control.
