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
http://graphics.stanford.edu/ba-colloquium/
Abstract
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.
Contact: bac-coordinators@cs.stanford.edu
Back to the Colloquium Page