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Learning Adaptive Models for Human-Robot Teaming

Tom Howard, Assistant Professor of Electrical and Computer Engineering

Abstract:

The efficiency and optimality of human-robot teams is often dictated by the fidelity and complexity of models for how a robot can interact with its environment, interpret the meaning of instructions, and/or exchange information with human partners.  It is common for researchers to engineer and/or learn these models a priori to achieve specific 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 and interact with humans must adapt to achieve, if not exceed, such levels of performance.  In this talk I will discuss progress towards model adaptation for robot intelligence in the context of human-robot teaming, including recent efforts in natural language understanding for human-robot interaction and robot motion planning. 

Biography:

Thomas Howard is a Wilmot 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 and Department of Computer Science, is an affiliate of the Goergen Institute of Data Science 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.

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 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, natural language understanding, and human-robot teaming.  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 2007 DARPA Urban Challenge.  Howard has earned Best Paper Awards at RSS (2016) and IEEE SMC (2017), two NASA Group Achievement Awards (2012, 2014), was a finalist for the ICRA Best Manipulation Paper Award (2012) and was recently selected for the NASA Early Career Faculty Award (2019).  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, National Aeronautics and Space Administration, and the New York State Center of Excellence in Data Science.

Faculty Page: http://www.hajim.rochester.edu/ece/people/faculty/howard_tom/index.html

Laboratory Page: http://www.ece.rochester.edu/projects/rail/