Learning in Artificial Sensorimotor Systems
Dr. Daniel D. Lee
Time and Place
Auditorium (NSH 1305)
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 information 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.
Daniel D. Lee is currently an Assistant Professor of
Electrical and Systems Engineering at the
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