Paul Robinette

I am an assistant professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Lowell (UML). I have performed extensive experiments on human-robot trust in time-critical situations in virtual simulations, the lab,  and the field. In recent years, I focused on field robotics: in-situ human-robot teaming experiments in the marine domain and field experiments for river navigation of autonomous surface vehicles. These projects have yielded datasets that have been released for the human-robot interaction community and the marine robotics community.

Before my current position, I worked as a Research Scientist at MIT in the Laboratory of Autonomous Marine Sensing Systems and the AUV Lab at MIT Sea Grant. While there, I worked on a number of projects including human-robot teaming, autonomy for surface vehicles, and perception in marine environments.
In 2016, I worked as a researcher at Georgia Tech Research Institute developing autonomy for unmanned aerial vehicles.
I received my Robotics PhD from Georgia Tech in December 2015. With my advisors Ayanna Howard and Alan Wagner, I investigated the ways that robot behaviors affect human trust in guidance robots during emergency evacuations. Through this research, we have evaluated the ability of robots to interact with humans in an understandable and trustworthy manner in studies involving over 2,000 participants. Overall, we found that people tend to overtrust robots in these situations:

Previously, I attended Missouri University of Science and Technology (S&T) (formerly University of Missouri-Rolla, UMR) where I received a Masters in Computer Engineering (advisor: Prof. Donald Wunsch)  and two Bachelors degrees (Computer Engineering and Physics).

Robotics is my passion and I have been hooked since I joined the S&T Robotics Competition Team the second semester of my freshmen year. While on the team, I held various positions, including Team President, as we competed annually in the Intelligent Ground Vehicle Competition. This competition requires the team to design and build a medium-sized robot to autonomously traverse an outdoor obstacle course.