Abstract

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children’s social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a “mental model” of the robot, tailoring the tutoring to the robot’s performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot’s bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.

Highlights

  • We argue that social human-robot interaction is an interesting means for extending machine learning

  • We report on a human-robot interaction (HRI) experiment in which a social robot acquires the meaning of linguistic labels using a variety of social learning strategies

  • We have presented an experiment in which a robot uses social cues during tutoring interactions with human teachers, which allowed the robot to modify and improve its learning input

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Summary

Introduction

We argue that social human-robot interaction is an interesting means for extending machine learning. It has been shown that robots can provide social cues to human interaction partners, but the social dimension of human-robot interaction is only being matched up with. Isolated aspects of the learning robot are considered, such as the physical design [3, 4]), instead we consider a holistic view of the learning interaction: from the learning strategies up to the social environment in which the learning is embedded. We report on a human-robot interaction (HRI) experiment in which a social robot acquires the meaning of linguistic labels using a variety of social learning strategies. We report on how the robot learns, and on how human teachers are sensitive to the robot’s social cues

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