Modeling the Development of Infant Imitation using Inverse Reinforcement Learning

Abstract

Little is known about the computational mechanisms of how imitation skills develop along with infant sensorimotor learning. In robotics, there are several well developed frameworks for imitation learning or so called learning by demonstration. Two paradigms dominate: Direct Learning (DL) and Inverse Reinforcement Learning (IRL). The former is a simple mechanism where the observed state and action pairs are associated to construct a copy of the action policy of the demonstrator. In the latter, an optimality principle or reward structure is sought that would explain the observed behavior as the optimal solution governed by the optimality principle or the reward function found. In this study, we explore the plausibility of whether some form of IRL mechanism in infants can facilitate imitation learning and understanding of others' behaviours. We propose that infants project the events taking place in the environment into their internal representations through a set of features that evolve during development. We implement this idea on a grid world environment, which can be considered as a simple model for reaching with obstacle avoidance. The observing infant has to imitate the demonstrator's reaching behavior through IRL by using various set of features that correspond to different stages of development. Our simulation results indicate that the U-shape performance change during imitation development observed in infants can be reproduced with the proposed model.