A Model-Based Goal-Directed Bayesian Framework for Imitation Learning in Humans and Machines

摘要

Imitation o#ers a powerful mechanism for knowledge acquisition, particularly for intelligent agents (like infants) that lack the ability to transfer knowledge using language. Several algorithms and models have recently been proposed for imitation learning in humans and robots. However, few proposals o#er a framework for imitation learning in noisy stochastic environments where the imitator must learn and act under real-time performance constraints. In this paper, we present a novel probabilistic framework for imitation learning in stochastic environments with unreliable sensors. We develop Bayesian algorithms, based on Meltzo# and Moore's AIM hypothesis for infant imitation, that implement the core of an imitation learning framework. Our algorithms are computationally e#cient, allowing real-time learning and imitation in an active stereo vision robotic head. We present results of both software simulations and our algorithms running on the head, demonstrating the validity of our approach. We conclude by advocating a research agenda that Preprint submitted to Elsevier Science 4 October 2004 promotes interaction between cognitive and robotic studies of imitation.