Event Location: NSH 1305
Bio: 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. 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 at JPL in the Mobility and Robotic Systems section, and a lecturer in mechanical engineering at Caltech. Prof. Howard earned a PhD in robotics from the Robotics Institute at Carnegie Mellon University in 2009 in addition to BS degrees in electrical and computer engineering and mechanical engineering from the University of Rochester in 2004. His research interests span artificial intelligence, robotics, and human-robot interaction with particular research focus on improving the optimality, efficiency, and fidelity of models for decision making in complex and unstructured environments with applications to robot motion planning and natural language understanding. He has applied his research on numerous robots including planetary rovers, autonomous automobiles, mobile manipulators, robotic torsos, and unmanned aerial vehicles. Prof. Howard was a member of the flight software team for the Mars Science Laboratory, the motion planning lead for the JPL Caltech DARPA Autonomous Robotic Manipulation team, and a member of Tartan Racing, winner of the DARPA Urban Challenge.

Abstract: 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.