Shaping Haru's Affective Behavior with Valence and Arousal Based Implicit Facial Feedback

Hui Wang, Guodong Chen, Randy Gomez, Keisuke Nakamura, Bo He, Guangliang Li

Abstract: Social robots that are able to express emotions can potentially improve human’s well-being. Whether and how they can learn from interactions between them and human being in a natural way will be key to their success and acceptance by ordinary people. In this paper, we proposed to shape social robot Haru affective behaviors with predicted continuous rewards based on received implicit facial feedback via human-centered reinforcement learning. The implicit facial feedback was estimated with the valence and arousal of received implicit facial feedback using Russell’s circumplex model, which can provide a more accurate estimation of the subtle psychological changes of human user, resulting in more effective robot behavior learning. The whole experiment is conducted on the desktop robot Haru, which is primarily used to study emotional interactions with human in different scenarios. Our experimental results show that with our proposed method, Haru can obtain a similar performance to learning from explicit feedback, eliminating the need for human users to get familiar with training interface in advance and resulting in an unobtrusive learning process.