Learning, introspection, and anticipation
for effective and reliable task planning
under uncertainty
Speaker: Prof. Gregory J. Stein
George Mason University
Friday, February 21, 2025
11:15AM- 12:15PM, ICAB 4110
Abstract
The next generation of service and assistive robots will need to operate under uncertainty, expected to complete tasks and perform well despite missing information about the state of the world or the future needs of itself and other agents. In this talk, I will present a number of recent and ongoing projects that improve long-horizon navigation and task planning in uncertain home-like environments. First, I will discuss our recent developments that improve performance and reliability in unfamiliar environments-environments potentially dissimilar from any seen during training-with a technique that enables fast and reliable deployment-time policy selection despite uncertainty. Second, I will discuss anticipatory planning, by which our robot anticipates and avoids side effects of its actions on undetermined future tasks it may later be assigned; our approach guides the robot towards behaviors that encourage preparation and organization, improving its performance over lengthy deployments.
Biography
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Prof. Gregory J. Stein is an Assistant Professor of CS at George Mason University, where he runs the Robotic Anticipatory Intelligence & Learning Group and is the director of the GMU Autonomous Robotics Lab. His research at the intersection of robotics, planning, and learning, develops approaches for planning and learning that allow robots to better understand the impact of their actions, so that they may plan quickly, intelligently, and reliably in a dynamic and uncertain world. Before joining Mason, he received his PhD in 2020 from MIT's EECS Department and previously graduated summa cum laude from Cornell University with a B.S. in Engineering Physics. His work was a finalist for Best Paper at the 2018 Conference on Robot Learning, at which he was additionally awarded Best Oral Presentation.
