University of Pennsylvania’s Daniel Lee presents “Decision Making in Robots and Animals” as part of the IRIM Robotics Seminar Series. The event will be held in the TSRB Banquet Hall from 12-1 p.m. and is open to the public.

Abstract

Current artificial intelligence systems for perception and action incorporate a number of techniques: optimal observer models, Bayesian filtering, probabilistic mapping, trajectory planning, dynamic navigation, and feedback control. I will briefly describe and demonstrate some of these methods for autonomous driving and for legged and flying robots, and contrast these models with neural representations and computation. I will also highlight and discuss the role of noise and differences between synthetic and biological approaches to decision making.

Bio

Daniel Lee is the UPS Foundation Chair Professor in the School of Engineering and Applied Science at the University of Pennsylvania. He received his B.A. summa cum laude in physics from Harvard University in 1990, and his Ph.D. in condensed matter physics from the Massachusetts Institute of Technology, in 1995.  Before coming to Penn, he was a researcher at AT&T and Lucent Bell Laboratories in the Theoretical Physics and Biological Computation departments.  He is a fellow of the IEEE and has received the National Science Foundation CAREER award and the University of Pennsylvania Lindback award for distinguished teaching. Lee was also a fellow of the Hebrew University Institute for Advanced Studies in Jerusalem, an affiliate of the Korea Advanced Institute of Science and Technology, and he organized the US-Japan National Academy of Engineering Frontiers of Engineering symposium.  As director of the GRASP Laboratory and co-director of the CMU-Penn University Transportation Center, his groups focus on understanding general computational principles in biological systems, and on applying that knowledge to build autonomous systems.