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Thomas Howard
University lecturer in electrical and computer engineering
Electrical and Computer Engineering, University of Rochester
October 8, 2021
Enabling grounded language communication for human-robot collaboration
Abstract: The ability of robots to effectively understand natural language instructions and convey information about their observations and interactions with the physical world relies heavily on the sophistication and fidelity of the robot's representations of language, environment, and actions. As we evolve toward 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 required by the cadence of human-robot interaction within the constraints of the platform's computational resources. In this talk, I will discuss my lab's research on algorithms and models for robot planning, mapping, control, and interaction with a specific focus on language-driven adaptive perception and bidirectional communication with deliberative interactive estimation.
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 and the Department of Computer Science, is affiliated with the Goergen Institute of Data Science, and directs the University of Rochester Robotics and Artificial Intelligence Laboratory. Previous appointments include a researcher and 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 Carnegie Mellon University's Robotics Institute in 2009, along with bachelor's degrees in electrical and computer engineering and mechanical engineering from the University of Rochester in 2004. His research interests include artificial intelligence, robotics, and human-robot interaction with a research focus on improving the optimality, efficiency, and reliability of decision-making models in complex and unstructured environments with applications to robot motion planning, natural language understanding, and human-robot collaboration. Howard was a member of the flight software team for the Mars Science Laboratory, the lead motion planner for the JPL/Caltech DARPA Autonomous Robotic Manipulation team, and a member of Tartan Racing, winner of the 2007 DARPA Urban Challenge. Howard has won Best Paper Awards from 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 the 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.
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