Learning Adaptive Models for Robot Motion Planning and Human-Robot Interaction

Tom Howard, University of Rochester


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 and robot motion planning.
Biosketch: Thomas Howard is an assistant professor in the Department of Electrical and Computer Engineering at the University of Rochester.  He also holds secondary appointments in the Department of Biomedical Engineering, Department of Computer Science, and Department of Neuroscience and directs the University of Rochester’s Robotics and Artificial Intelligence Laboratory. 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 the Jet Propulsion Laboratory in the Robotic Software Systems Group, and a lecturer in mechanical engineering at Caltech and was a Goergen Institute for Data Science Center of Excellence Distinguished Researcher.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.  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.  Howard has earned Best Paper Awards at RSS (2016) and IEEE SMC (2017), two NASA Group Achievement Awards (2012, 2014), and was a finalist for the ICRA Best Manipulation Paper Award (2012).  Howard’s research at the University of Rochester has been supported by National Science Foundation, Army Research Office, Army Research Laboratory, Department of Defense Congressionally Directed Medical Research Program, and the New York State Center of Excellence in Data Science.