The Human in Human-Robot Interaction
Thursday, April 28, 2022, 04:00pm
Speaker: Matthew Gombolay
Dr. Matthew Gombolay is an Assistant Professor of Interactive Computing at the Georgia Institute of Technology. He received a B.S. in Mechanical Engineering from the Johns Hopkins University in 2011, an S.M. in Aeronautics and Astronautics from MIT in 2013, and a Ph.D. in Autonomous Systems from MIT in 2017. Gombolay’s research interests span robotics, AI/ML, human-robot interaction, and operations research. Between defending his dissertation and joining the faculty at Georgia Tech, Dr. Gombolay served as technical staff at MIT Lincoln Laboratory, transitioning his research to the U.S. Navy and earning an R&D 100 Award. His publication record includes a best paper award from the ACM/IEEE International Conference on Human-Robot Interaction, and the American Institute for Aeronautics and Astronautics, a finalist for best paper at the Conference on Robot Learning, and a finalist for best student paper at the American Controls Conference. Dr Gombolay was selected as a DARPA Riser in 2018, received 1st place for the Early Career Award from the National Fire Control Symposium, and was awarded a NASA Early Career Fellowship.
This talk is organized by the Rutgers SOCRATES project.
Location : Via Zoom
Event Type: Seminar
Abstract: 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 in human-in-the-loop systems. 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 human-autonomy interaction. In my talk, I will focus on our recent work (1) democratizing robot learning by formulating better models of heterogeneous and suboptimal human teachers; (2) explaining to those humans what the robot has learned to facilitate shared mental models; and (3) scaling the human-robot interaction to team-level coordination with graph neural networks. The goal of this research is to inform the design of autonomous teammates so that users want to turn – and benefit from turning – autonomy to the O-N mode.
Rutgers University School of Arts and Sciences
Contact Host: Kostas Bekris