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arXiv · 1804.05259

Intrinsically motivated reinforcement learning for human-robot interaction in the real-world

Ahmed Hussain Qureshi·Yutaka Nakamura·Yuichiro Yoshikawa·Hiroshi Ishiguro
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Abstract

For a natural social human-robot interaction, it is essential for a robot to learn the human-like social skills. However, learning such skills is notoriously hard due to the limited availability of direct instructions from people to teach a robot. In this paper, we propose an intrinsically motivated reinforcement learning framework in which an agent gets the intrinsic motivation-based rewards through the action-conditional predictive model. By using the proposed method, the robot learned the social skills from the human-robot interaction experiences gathered in the real uncontrolled environments. The results indicate that the robot not only acquired human-like social skills but also took more human-like decisions, on a test dataset, than a robot which received direct rewards for the task achievement.

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Ahmed Hussain Qureshi, Yutaka Nakamura, Yuichiro Yoshikawa, Hiroshi Ishiguro. 2018-04-14. Intrinsically motivated reinforcement learning for human-robot interaction in the real-world. https://doi.org/10.1016/j.neunet.2018.03.014

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