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X-ORIGINAL-URL:https://optimisation.doc.ic.ac.uk
X-WR-CALDESC:Events for Computational Optimisation Group
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DTSTART:20130101T000000
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DTSTART;TZID=UTC:20141017T150000
DTEND;TZID=UTC:20141017T150000
DTSTAMP:20260404T083534
CREATED:20170124T102139Z
LAST-MODIFIED:20170124T102139Z
UID:567-1413558000-1413558000@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Bayesian Optimization for Learning Robot Control
DESCRIPTION:Title: Bayesian Optimization for Learning Robot ControlSpeaker: Dr. Marc DeisenrothAffiliation: Department of Computing – Imperial College LondonLocation: Room 217 Huxley BuildingTime: 3:00pm \nAbstract. Statistical machine learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows us to reduce the amount of engineering knowledge that is otherwise required. In real systems\, such as robots\, many experiments can be impractical and time consuming.  I will discuss Bayesian optimization\, an approach to controller learning that is based on efficient global optimization of black-box (utility) functions\, in the context of robot learning. I will demonstrate that this kind of learning is (a) practical and (b) very fast\, i.e.\, it requires only a few experiments\, to learn good controller parameterizations for a bipedal robot. \nAbout the speaker. Marc is PI of the SML group and an Imperial College Junior Research Fellow (tenure-track) with interests in statistical machine learning\, robotics\, control\, time-series analysis\, and signal processing. Marc joined the Department of Computing as a Research Fellow in September 2013. From December 2011 to August 2013 he was a Senior Research Scientist & Group Leader (Learning for Control) at TU Darmstadt (Germany). Marc is still adjunct researcher at TU Darmstadt. From February 2010 to December 2011\, he was a full-time Research Associate at the University of Washington (Seattle). Marc completed his PhD at the Karlsruhe Institute for Technology (Germany) in 2009. He conducted his PhD research at the Max Planck Institute for Biological Cybernetics (2006–2007) and at the University of Cambridge (2007–2009).  Marc’s research interests center around methodologies from modern Bayesian machine learning and their application  autonomous control and robotic systems. Marc’s goal is to increase the level of autonomy in robotic and control systems by modeling and accounting for uncertainty in a principled way. Potential applications include intelligent prostheses\, autonomous robots\, and healthcare assistants.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-bayesian-optimization-for-learning-robot-control/
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