A Bayesian Model of Imitation in Infants and Robots

Learning through imitation is a powerful and versatile method for acquiring new behaviors. In humans, a wide range of behaviors, from styles of social interaction to tool use, are passed from one generation to another through imitative learning. Although imitation evolved through Darwinian means, it achieves Lamarckian ends: it is a mechanism for the inheritance of acquired characteristics. Unlike trial-and-error-based learning methods such as reinforcement learning, imitation allows rapid learning. The potential for rapid behavior acquisition through demonstration has made imitation learning an increasingly attractive alternative to manually programming robots. In this chapter, we review recent results on how infants learn through imitation and discuss Meltzoff and Moore's four-stage progression of imitative abilities: (i) body babbling, (ii) imitation of body movements, (iii) imitation of actions on objects, and (iv) imitation based on inferring intentions of others. We formalize these four stages within a probabilistic framework for learning and inference. The framework acknowledges the role of internal models in sensorimotor control and draws on recent ideas from the field of machine learning regarding Bayesian inference in graphical models. We highlight two advantages of the probabilistic approach: (1) the development of new algorithms for imitation-based learning in robots acting in noisy and uncertain environments, and (2) the potential for using Bayesian methodologies (such as manipulation of prior probabilities) and robotic technologies to deepen our understanding of imitative learning in humans.