A Probabilistic Framework for Model-Based Imitation Learning

Humans and animals use imitation as a mechanism for acquiring knowledge. Recently, several algorithms and models have been proposed for imitation learning in robots and humans. However, few proposals offer a framework for imitation learning in a stochastic environment where the imitator must learn and act under real-time performance constraints. We present a 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, and sketch basic proposals for the other components. 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.