Title:
The Science of Autonomy: A "Happy" Symbiosis Among Control, Learning and Physics

dc.contributor.author Theodorou, Evangelos A.
dc.contributor.corporatename Georgia Institute of Technology. Machine Learning en_US
dc.contributor.corporatename Georgia Institute of Technology. School of Aerospace Engineering en_US
dc.date.accessioned 2018-04-09T18:40:44Z
dc.date.available 2018-04-09T18:40:44Z
dc.date.issued 2018-03-28
dc.description Presented on March 28, 2018 at 12:00 p.m. in the Marcus Nanotechnology Building, room 1116. en_US
dc.description Evangelos 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.description Runtime: 62:09 minutes en_US
dc.description.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 learning and control. en_US
dc.format.extent 62:09 minutes
dc.identifier.uri http://hdl.handle.net/1853/59511
dc.language.iso en_US en_US
dc.relation.ispartofseries Machine Learning @ Georgia Tech (ML@GT) Seminar Series
dc.subject Autonomy en_US
dc.subject Decision making en_US
dc.subject Inference en_US
dc.subject Stochastic optimal control en_US
dc.title The Science of Autonomy: A "Happy" Symbiosis Among Control, Learning and Physics en_US
dc.type Moving Image
dc.type.genre Lecture
dspace.entity.type Publication
local.contributor.author Theodorou, Evangelos A.
local.contributor.corporatename Machine Learning Center
local.contributor.corporatename College of Computing
local.relation.ispartofseries ML@GT Seminar Series
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relation.isOrgUnitOfPublication 46450b94-7ae8-4849-a910-5ae38611c691
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isSeriesOfPublication 9fb2e77c-08ff-46d7-b903-747cf7406244
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