Affective Behavior Learning for Social Robot Haru with Implicit Evaluative Feedback

Abstract

We propose a human-in-the-loop reinforcement learning mechanism to help robots learn emotional behavior. Unlike the previous methods of providing explicit feedback via pressing keyboard buttons or mouse clicks, we provide a more natural way for ordinary people to train social robots how to perform social tasks according to their preferences - facial expressions. The whole experiment is carried out on the desktop robot Haru, which is mainly used for the research of emotion and empathy participation. Our experimental results show that through learning from implicit feedback of facial features, Haru can quickly understand and dynamically adapt to individual preferences, and obtain a similar performance to learning from explicit feedback. In addition, we observe that the recognition error of human feedback will cause a “temporary regress” of the robot's learning performance, which is more obvious at the beginning of the training process. This phenomenon is shown to be correlated with the accuracy of recognizing negative implicit feedback.