Bootstrapping Imitation via Vision Based Shared Attention
by
Matt Hoffman
Shared attention refers to the simultaneous perceptual focus of two or
more agents on a single object in their shared environment. Endowing
robots with the capacity for shared attention can lead to systems
capable of complex, natural forms of learning, such as learning by
imitation. We discuss various methods of building shared attention on a
robotic platform. Our methods are based on Meltzoff and Moore's AIM
model for imitation in infants. Bayesian algorithms implement the core
of a shared attention framework that follows the gaze and
finger-pointing of an instructor and learns task-specific saliency
models from a cluttered scene. Our results demonstrate the value of
our system for promoting interaction between humans and robots.
Advised by Raj Rao
CSE 403
Wednesday
May 11, 2005
3:30 - 4:20 pm