Title: Intrinsically motivated reinforcement learning for human-robot interaction in the real-world,
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.