Exploiting social partners in robot learning

Abstract

Social learning in robotics has largely focused on imitation learning. Here we take a broader view and are interested in the multifaceted ways that a social partner can in uence the learning process. We implement four social learning mechanisms on a robot: stimulus enhancement, emulation, mimicking, and imitation, and illustrate the computational benefits of each. In particular, we illustrate that some strategies are about directing the attention of the learner to objects and others are about actions. Taken together these strategies form a rich repertoire allowing social learners to use a social partner to greatly impact their learning process. We demonstrate these results in simulation and with physical robot `playmates'.