The SIAI seminar series features speakers from academia, research labs, government agencies and the industry in topics of interest to the SIAI community.
Learning, Introspection, and Anticipation for Effective and Reliable Task Planning Under Uncertainty: Towards Household Robots Comfortable with Missing Knowledge
Gregory J. Stein, Ph.D.
Assistant Professor, Computer Science, George Mason University and Director, GMU Autonomous Robotics Lab
Friday, September 27, 2024 at 11 a.m.
University Center Complex, Gallery
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. Many existing approaches turn to learning to overcome the challenges of planning under uncertainty, yet are often brittle or myopic, limiting their effectiveness. Our work introduces a family of model-based approaches to long-horizon planning under uncertainty that augment (rather than replace) planning with estimates from learning, allowing for both high performance and reliability by design.
In this talk, Prof. Stein will present a number of recent and ongoing projects that improve long-horizon navigation and task planning in uncertain home-like environments. First, he will discuss his recent developments that improve performance and reliability in unfamiliar environments—environments potentially dissimilar from any seen during training—with a technique he calls "offline alt-policy replay." This technique enables fast and reliable deployment-time policy selection despite uncertainty. Second, Prof. Stein will discuss "anticipatory planning," by which his robot anticipates and avoids side effects of its actions on undetermined future tasks it may later be assigned; his approach guides the robot towards behaviors that encourage preparation and organization, improving its performance over lengthy deployments.
About the Speaker
Gregory J. Stein is an Assistant Professor of Computer Science at George Mason University (GMU), where he runs the Robotic Anticipatory Intelligence & Learning (RAIL) Group and is the director of the GMU Autonomous Robotics Lab. His research, at the intersection of robotics, planning, and machine learning, is centered around developing representations 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 GMU, Greg received his Ph.D. from MIT’s Department of Electrical Engineering and Computer Science (2020). He previously graduated summa cum laude from Cornell University with a B.S. in Applied and 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.
Please visit Prof. Stein's website to learn more.