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.
About the Speaker: Thomas Howard is an Assistant Professor in the Department of Computer Science and the Department of Electrical and Computer Engineering at the University of Rochester. He also holds a secondary appointment in the Department of Biomedical Engineering and is a member of the Institute for Data Science. Previously he held appointments as a Research Scientist and a Postdoctoral Associate at MIT's Computer Science and Artificial Intelligence Laboratory in the Robust Robotics Group, a Research Technologist II at the Jet Propulsion Laboratory in the Robotic Software Systems group, and a Lecturer in Mechanical Engineering at Caltech. Prof. Howard earned his Ph.D. in Robotics from Carnegie Mellon University and Bachelor of Science degrees in Mechanical Engineering and Electrical and Computer Engineering from the University of Rochester.