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dc.contributor.authorTheodorou, Evangelos A.
dc.date.accessioned2018-04-09T18:40:44Z
dc.date.available2018-04-09T18:40:44Z
dc.date.issued2018-03-28
dc.identifier.urihttp://hdl.handle.net/1853/59511
dc.descriptionPresented on March 28, 2018 at 12:00 p.m. in the Marcus Nanotechnology Building, room 1116.en_US
dc.descriptionEvangelos Theodorou is an Assistant Professor in the School of Aerospace Engineering at Georgia Tech. His theoretical research spans the areas of control theory, machine learning, information theory and statistical physics. Applications involve autonomous planning and control in robotics and aerospace systems, bio-inspired control and design.en_US
dc.descriptionRuntime: 62:09 minutesen_US
dc.description.abstractIn 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 learning and control.en_US
dc.format.extent62:09 minutes
dc.language.isoen_USen_US
dc.relation.ispartofseriesMachine Learning @ Georgia Tech (ML@GT) Seminaren_US
dc.subjectAutonomyen_US
dc.subjectDecision makingen_US
dc.subjectInferenceen_US
dc.subjectStochastic optimal controlen_US
dc.titleThe Science of Autonomy: A "Happy" Symbiosis Among Control, Learning and Physicsen_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Machine Learningen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Aerospace Engineeringen_US


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