CSE Profile: Paul Schrater

Paul Schrater
Associate Professor

(612) 626-1839
Office: EE/CS 5-187

Interests

Human and computer vision, motor control & haptics, statistical inference, pattern recognition & Bayesian networks, virtual reality

Education

Ph.D. 1999, Neuroscience, University of Pennsylvania

B.S., 1992, Neuroscience, California State University, Long Beach

Research

My research interests include human and computer vision, planning and guiding reaches with and without visual information, and the integration of visual, haptic, and motor information during the perception-action cycle. My research approach treats problems in vision and motor control as problems of statistical inference, which has led to a concurrent interest in statistical methods that includes Bayesian (Belief) Networks, Dynamic Markov Decision Networks, Pattern Theory, Machine Learning, and other topics in statistics and pattern recognition.

Over the next few years, I plan to build an integrated haptic (touch) and visual Virtual Reality Laboratory to explore questions about how humans use visual and haptic information to perform tasks like reaching and grasping a slippery object in motion. In order to accomplish such a task, the visual system needs to supply information about the material properties (friction & mass), shape, location, and motion of the object before and during the reach. At the end of the reach, the fingers need to be appropriately positioned such that they can generate forces sufficient to maintain grasp stability without smashing or dropping the object. My goal is to understand how humans perform such tasks at a level that would allow us to produce robots that can perform as well or better on the same tasks. The research involves using Bayesian Decision Theory to theoretically analyze optimal and sub-optimal strategies for performing a task coupled with rigorous psychophysical experiments that can determine the strategies and information human observers actually use to perform the task. The results should provide important insights into the design of humanoid robots, the design of human-computer interfaces and interaction, and how the human brain works.

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