Computational benefits of social learning mechanisms: Stimulus enhancement and emulation

Social learning in robotics has largely focused on imitation learning. In this work, we take a broader view of social learning and are interested in the multifaceted ways that a social partner can influence the learning process. We implement stimulus enhancement and emulation on a robot, and illustrate the computational benefits of social learning over individual learning. Additionally we characterize the differences between these two social learning strategies, showing that the preferred strategy is dependent on the current behavior of the social partner. We demonstrate these learning results both in simulation and with physical robot ‘playmates’.

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