Stochastic Control: From Theory to Parallel Computation and Applications

Theodorou, Evangelos A.

Title:

Stochastic Control: From Theory to Parallel Computation and Applications

dc.contributor.author	Theodorou, Evangelos A.
dc.contributor.corporatename	Georgia Institute of Technology. Institute for Robotics and Intelligent Machine	en_US
dc.contributor.corporatename	Georgia Institute of Technology. School of Aerospace Engineering	en_US
dc.date.accessioned	2016-03-02T20:08:08Z
dc.date.available	2016-03-02T20:08:08Z
dc.date.issued	2016-02-24
dc.description	Presented on February 24, 2016 at 12:00 p.m. in the TSRB Banquet Hall.	en_US
dc.description	Evangelos A. Theodorou is an assistant professor in the Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology. He is also affiliated with the Institute of Robotics and Intelligent Machines. His research interests span the areas of stochastic optimal control, machine learning, statistical physics, and computational neuroscience.
dc.description	Runtime: 60:03 minutes
dc.description.abstract	For autonomous systems to operate in stochastic environments, they have to be equipped with fast decision-making processes to reason about the best possible action. Grounded on first principles in stochastic optimal control theory and statistical physics, the path integral framework provides a mathematically sound methodology for decision making under uncertainty. It also creates opportunities for the development of novel sampling-based planning and control algorithms that are highly parallelizable. In this talk, I will present results in the area of sampling-based control that go beyond classical formulations and show applications to robotics and autonomous systems for tasks such as manipulation, grasping, and high-speed navigation. In addition to sampling-based stochastic control, alternative methods that rely on uncertainty propagation using stochastic variational integrators and polynomial chaos theory will be presented and their implications to trajectory optimization and state estimation will be demonstrated. At the end of this talk, and towards closing the gap between high-level reasoning/decision making and low-level organization/computation, I will highlight the interdependencies between theory, algorithms, and forms of computation and discuss future computational technologies in the area of autonomy and robotics.	en_US
dc.embargo.terms	null	en_US
dc.format.extent	60:03 minutes
dc.identifier.uri	http://hdl.handle.net/1853/54554
dc.relation.ispartofseries	IRIM Seminar Series
dc.subject	Autonomous systems	en_US
dc.subject	Robotics	en_US
dc.subject	Sampling-based control	en_US
dc.subject	Stochastic environments	en_US
dc.title	Stochastic Control: From Theory to Parallel Computation and Applications	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.author	Theodorou, Evangelos A.
local.contributor.corporatename	Institute for Robotics and Intelligent Machines (IRIM)
local.relation.ispartofseries	IRIM Seminar Series
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