A Model-Based Goal-Directed Bayesian Framework for Imitation Learning in Humans and Machines
Imitation offers 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 offer 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 Meltzoff and Moore’s AIM hypothesis for infant imitation, that implement the core of an imitation learning framework. Our algorithms are computationally efficient, 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