Lockheed Martin Robotics Seminar
Modern Challenges in Motion Planning for Autonomous Robots
Department of Computer Science and Engineering
University of Minnesota
Motion planning forms a central part of modern autonomous robotics systems by providing the “intelligence’’ to allow robots to determine how to move through the environment and safely achieve their goals. The past two decades have seen tremendous progress in new motion planning algorithms based on techniques such as search trees, POMDP solvers, belief-space planning, and reinforcement learning, that can plan robot motion in challenging environments. However, most of these techniques work best in structured environments where the robot is the only activity entity, and where all of the environment's state is readily known. In practice, robots are increasingly needed in unstructured environments, where they may need to operate in the presence of other robots (and people), often under conditions of high uncertainty. In this talk, I will cover some of my recent work as it relates to addressing these challenges including strategies to improve autonomous robot navigation in uncertain, dynamic environments and I will discuss very recent advances in planning techniques for multiple agents in shared spaces. I will also present new methods to measure and improve the quality of predictive simulations of human motion and discuss how these improved simulations can lead to better trajectories for robots moving in shared environments with people.
Mumu Xu and Dinesh Manocha
Stephen J. Guy is an associate professor in the Department of Computer Science and Engineering at the University of Minnesota. His research focuses on the development of artificial intelligence for use in computer simulations (e.g., crowd simulation and intelligent virtual characters) and autonomous robotics (e.g., collision avoidance and path planning under uncertainty). Stephen’s work has had a wide influence in games, VR, and real-time graphics industries: his work on motion planning has been licensed by Relic Entertainment, EA, and other digital entertainment companies; he has been a speaker in the AI Summit at GDC, the leading conference in the games development industry; and he currently serves as the vice chair of the board of directors for Glitch, a non-profit focused on empowering and diversifying the talent pool of game developers and creators. He is the recipient of several awards including the Charles E. Bowers Faculty Teaching Award and best paper awards for his work in crowd simulation and on applications of stochastic tree search in games. Stephen’s academic work has appeared in top venues for robotics, AI and computer graphics including SIGGRAPH, IJRR, IEEE Trans. on Robotics, AAMAS, AAAI, and IJCAI. His work on simulating virtual humans has been widely covered in popular media including newspapers, magazines, documentaries, and late-night TV. Prior to joining Minnesota, he received his Ph.D. in Computer Science in 2012 from the University of North Carolina - Chapel Hill with support from fellowships from Google, Intel, and the UNCF, and his B.S. in Computer Engineering with honors from the University of Virginia in 2006.