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dc.contributor.authorTheodorou, Evangelos A.
dc.date.accessioned2016-03-02T20:08:08Z
dc.date.available2016-03-02T20:08:08Z
dc.date.issued2016-02-24
dc.identifier.urihttp://hdl.handle.net/1853/54554
dc.descriptionPresented on February 24, 2016 at 12:00 p.m. in the TSRB Banquet Hall.en_US
dc.descriptionEvangelos 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.descriptionRuntime: 60:03 minutes
dc.description.abstractFor 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.format.extent00:00 minutes
dc.format.extent60:03 minutes
dc.relation.ispartofseriesIRIM Seminar Seriesen_US
dc.subjectAutonomous systemsen_US
dc.subjectRoboticsen_US
dc.subjectSampling-based controlen_US
dc.subjectStochastic environmentsen_US
dc.titleStochastic Control: From Theory to Parallel Computation and Applicationsen_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machineen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Aerospace Engineeringen_US
dc.embargo.termsnullen_US


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  • IRIM Seminar Series [124]
    Each semester a core seminar series is announced featuring guest speakers from around the world and from varying backgrounds in robotics.

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