Optimal sensory encoding and robot perception and navigation
August 7, 2012 1:30 PM
Consider the difficulty of an autonomous robot performing a search and rescue mission in a hazardous environment containing broken walls, rubble, fire, smoke, and fog.
How is it possible to sense and navigate in such noisy and uncertain environments? I will first review recently developed algorithms for mapping, path planning, and locomotion control, and then show how biological neurons are able to encode perceptual information in a Bayes optimal manner. In particular, I will discuss our recent results relating the Fisher Information measure to optimal encoding using generalized Cramer-Rao bounds.
This page is
maintained by Yumi Yi (firstname.lastname@example.org).