Democratizing Robot Learning for Safe, Efficient Human-Robot Interaction
Abstract:
Robotic technology offers the promise of performing at-home care tasks, revitalizing manufacturing, and even scaling the power of earth-bound scientists in autonomous space exploration. However, each new robot deployment today requires an ad hoc army of consultants and vast computing resources operating on black box, sample-inefficient models. To unlock the potential of robotics, we need to democratize machine learning and put the power of these tools in the hands of the end user. In this talk, I will present exciting, novel work in my lab that enables robots to safely and efficiently learn from human teachers and interactions with their environments. I will demonstrate how we can 1) enable robots to learn new skills from heterogeneous human teachers, 2) balance the need to actively learn more about their environment while remaining safe in proximity to humans, and 3) and convey their knowledge through interpretable and explainable machine learning models.
Biodata:
Dr. Matthew Gombolay is the Anne and Alan Taetle 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, a 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 a technical staff member at MIT's Lincoln Laboratory transitioning his research to the U.S. Navy, earning him an R&D 100 Award. His publication record includes a best paper award from American Institute for Aeronautics and Astronautics, and he was selected as a DARPA Riser in 2018. He was also awarded a NASA Early Career Fellowship for his work increasing science autonomy in space.