An emotional model for social robots

Abstract

We developed an emotional model, which could help supporting robots to accommodate humans during a working task inside an industrial setting. The robot would recognize when a human is experiencing increased stress and decides whether it should assist the human or should do other tasks. We propose the model as a framework which was developed as part of "The Smart Virtual Worker"-project within the context of human-robot interactions. The emotional model is able to estimate a worker's emotional valence throughout a typical work task by applying a hierarchical reinforcement learning algorithm. Since emotions are generated by the human brain based on an individual's interpretation of a stimulus, we linked the genesis of emotions to empirical findings of the sports sciences in order to infer an emotional reaction. Furthermore, the model reproduces sympathetic reactions of the human body and is capable of remembering past actions in order to include possible future time constraints as an initiator for emotional responses in the upcoming iterations. This capability is crucial for accommodating long-term experiences since the emotional reaction is not only based on the present situation, but on the whole experimental setting.