Title:
The Science of Autonomy: A "Happy" Symbiosis Among Control, Learning and Physics
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 | |
relation.isAuthorOfPublication | aa760d2f-a820-43f1-b1ea-bcb6bfab8b13 | |
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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