Probabilistic Gaze Imitation and Saliency Learning in a Robotic Head (2004)
| Citations: | 4 - 0 self |
BibTeX
@MISC{Shon04probabilisticgaze,
author = {Aaron P. Shon and David B. Grimes and Chris L. Baker and Matthew W. Hoffman and Shengl Zhou and Rajesh P. N. Rao},
title = {Probabilistic Gaze Imitation and Saliency Learning in a Robotic Head},
year = {2004}
}
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Abstract
Imitation is a powerful mechanism for transferring knowledge from an instructor to a naive observer. We first present Bayesian algorithms, based on Meltzoff and Moore's AIM model for imitation in infants, that implement the core of an imitation learning framework. Next, we present Bayesian algorithms for learning which objects an instructor considers salient in a task. Finally, we demonstrate the performance of our algorithms in a gaze following and saliency learning task implemented on an active vision robotic head. Our results suggest that the ability to follow gaze and learn instructor- and task-specific saliency models could play a crucial role in building systems capable of complex forms of humanrobot interaction.
