Professor Dr. Matthew Gombolay

New advances in robotics and autonomy offer a promise of revitalizing final assembly manufacturing, assisting in personalized at-home healthcare, and even scaling the power of earth-bound scientists for robotic space exploration. Yet, in real-world applications, autonomy is often run in the O-F-F mode because researchers fail to understand the human end-user’s goals, needs, and wishes. In this talk, I will share exciting research we are conducting at the nexus of human factors engineering and cognitive robotics to inform the design of explainable and interactive machine learning systems. In my talk, I will focus on our recent work on enabling machines to learn human-interpretable decision-making and control policies from interaction with their environments. I will show that our novel explainable Artificial Intelligence (XAI) methods not only improves subjective measures of end-user interaction but also improves objective performance in human-machine teaming.

Dr. Matthew Gombolay is an Assistant Professor of Interactive Computing at Georgia Tech (GT) and was named Anne and Alan Taetle Early-Career Assistant Professor in 2018. He received a B.S. Mechanical Engineering from Johns Hopkins University in 2011, an S.M. AeroAstro from MIT in 2013, and a Ph.D. Autonomous Systems from MIT in 2017. Dr. Gombolay was a technical staff at MIT Lincoln Laboratory from 2017-2018, earning an R&D 100 Award for transitioning research to the Navy. At GT, Dr. Gombolay founded the Cognitive Optimization and Relational (CORE) Robotics Lab, which places the power of robots in the hands of diverse, non-expert end-users by developing new computational methods and human factors insights. Dr. Gombolay’s research lab has received numerous best paper awards and nominations, including most recently at the 2022 ACM/IEEE International Conference on Human-Robot Interaction. Dr. Gombolay is a DARPA Riser, NASA Early Career Fellow, and an Associate Editor of Autonomous Robots and ACM Transactions on Human-Robot Interaction.

Location: The seminar will be delivered online via zoom.
https://unitn.zoom.us/j/82021320646
Meeting ID: 820 2132 0646
Passcode: 564616

PDF & VIDEO