Broad Area Colloquium For AI-Geometry-Graphics-Robotics-Vision
(CS 528)

Learning in Artificial Sensorimotor Systems

Dan Lee
Feburary 21, 2005, 4:15PM
TCSeq 200


Many algorithms in machine learning involve changing the underlying dimensionality of the data set. Unsupervised learning techniques such as principal components analysis typically involve dimensionality reduction, whereas supervised learning techniques such as support vector machines can be understood as mapping the data to a higher dimensional space. Equivalent problems emerge when considering processing in sensorimotor systems. Sensory processing requires mapping high-dimensional sensory inputs onto a smaller number of perceptually-relevant features, whereas motor learning involves driving a large number of actuator parameters with a smaller number of control variables. I will describe some of our recently developed learning algorithms that utilize changes in dimensionality, and demonstrate their application on some prototypical robotic systems.

About the Speaker

Daniel D. Lee is currently an Assistant Professor of Electrical and Systems Engineering at the University of Pennsylvania, with a secondary appointment in the Department of Bioengineering. He received his B.A. in Physics from Harvard University in 1990, and his Ph.D. in Condensed Matter Physics from the Massachusetts Institute of Technology in 1995. He was a researcher at Bell Laboratories, Lucent Technologies, from 1995-2001 in the Theoretical Physics and Biological Computation departments. His research focuses on understanding the general principles that biological systems use to process and organize information, and on applying that knowledge to build better artificial sensorimotor systems. He resides in New Jersey with his wife Lisa, three-year old son Jordan, and daughter Jessica who just turned one.


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