The ability for robots to effectively understand natural language instructions and convey information about their observations and interactions with the physical world is highly dependent on the sophistication and fidelity of the robot’s representations of language, environment, and actions.  As we progress towards more intelligent systems that perform a wider range of tasks in a greater variety of domains, we need models that can adapt their representations of language and environment to achieve the real-time performance necessitated by the cadence of human-robot interaction within the computational resource constraints of the platform.  In this talk I will review my laboratory’s research on algorithms and models for robot planning, mapping, control, and interaction with a specific focus on language-guided adaptive perception and bi-directional communication with deliberative interactive estimation.    

Bio: 

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 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 a 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 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 Host: Nikolay Atanasov - ECE