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Monroe D Kennedy
Assistant Professor, Mechanical Engineering
Assistant Professor (By courtesy), Computer Science
Member, Bio-X
Faculty Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)
Member, Wu Tsai Human Performance Alliance
PhD, University of Pennsylvania, Mechanical Engineering and Applied Mechanics (2019)
MS, University of Pennsylvania, Robotics (2016)
BS, University of Maryland, Baltimore County, Mechanical Engineering (2012)
Monroe Kennedy III is an Assistant Professor of Mechanical Engineering, with a courtesy appointment in Computer Science. He is the recipient of the NSF Career Award. He received his Ph.D. in Mechanical Engineering and Applied Mechanics, and a Masters in Robotics from the University of Pennsylvania where he was a recipient of both the NSF and GEM graduate research fellowships. His area of expertise is in collaborative robotics, specifically the development of theoretical and experimental approaches to enhance robotic autonomy and robotic effectiveness in decentralized tasks toward human-robot collaboration. He applies expertise in machine learning, computer vision, collaborative robot teammate intent estimation, dynamical systems analysis, control theory (classical, non-linear, and robust control), state estimation and prediction, and motion planning.
He is the director of the Assistive Robotics and Manipulation Lab (ARMLab) whose broad research objective is to develop technology that improves everyday life by anticipating and acting on the needs of human counterparts. ARMLab specializes in developing intelligent robotic systems that can perceive and model environments, humans, and tasks and leverage these models to predict system processes and understand their assistive role. The research can be divided into the following sub-categories: robotic assistants, connected devices, and intelligent wearables. ARMLab research requires the use of a combination of tools in dynamical systems analysis, control theory (classical, non-linear, and robust control), state estimation and prediction, motion planning, vision for robotic autonomy, teammate intent estimation, and machine learning. ARMLab focuses heavily on both the analytical and experimental components of collaborative robotics. Research applications include autonomous assistive technology, robotic assistants (mobile manipulators and humanoids) with the goal of deployment for service tasks that may be highly dynamic and require dexterity, situational awareness, and human-robot collaboration.
He is the director of the Assistive Robotics and Manipulation Lab (ARMLab) whose broad research objective is to develop technology that improves everyday life by anticipating and acting on the needs of human counterparts. ARMLab specializes in developing intelligent robotic systems that can perceive and model environments, humans, and tasks and leverage these models to predict system processes and understand their assistive role. The research can be divided into the following sub-categories: robotic assistants, connected devices, and intelligent wearables. ARMLab research requires the use of a combination of tools in dynamical systems analysis, control theory (classical, non-linear, and robust control), state estimation and prediction, motion planning, vision for robotic autonomy, teammate intent estimation, and machine learning. ARMLab focuses heavily on both the analytical and experimental components of collaborative robotics. Research applications include autonomous assistive technology, robotic assistants (mobile manipulators and humanoids) with the goal of deployment for service tasks that may be highly dynamic and require dexterity, situational awareness, and human-robot collaboration.
