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Theodorou, Evangelos A.

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End date: 2018 × Start date: 2014 × Has files: Yes × search.filters.applied.f.resourcesubtype: Lecture ×

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    The Science of Autonomy: A "Happy" Symbiosis Among Control, Learning and Physics
    ( 2018-03-28) Theodorou, Evangelos A.
    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.
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    Stochastic Control: From Theory to Parallel Computation and Applications
    ( 2016-02-24) Theodorou, Evangelos A.
    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.