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Thomas Howard: Learning models for robot decision making | CMU RI Seminar


curated by | February 27, 2016

“Thomas Howard is an assistant professor in the Department of Electrical and Computer Engineering and the Department of Computer Science. He is also a member of the Institute for Data Science and holds a secondary appointment in the Department of Biomedical Engineering.” His abstract follows: “The efficiency and optimality of robot decision making is often dictated by the fidelity and complexity of models for how a robot can interact with its environment. It is common for researchers to engineer these models a priori to achieve particular levels of performance for specific tasks in a restricted set of environments and initial conditions. As we progress towards more intelligent systems that perform a wider range of objectives in a greater variety of domains, the models for how robots make decisions must adapt to achieve, if not exceed, engineered levels of performance. In this talk I will discuss progress towards model adaptation for robot intelligence, including recent efforts in natural language understanding for human-robot interaction.”



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Presented work at IROS 2018 (Part 1 of 3)
November 12, 2018

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