Emotional Bodily Expressions for Culturally Competent Robots through Long Term Human-Robot Interaction

Generating emotional bodily expressions for culturally competent robots has been gaining increased attention to enhance the engagement and empathy between robots and humans in a multi-culture society. In this paper, we propose an incremental learning model for selecting the user's representative or habitual emotional behaviors which place emphasis on individual users' cultural traits identified through long term interaction. Furthermore, a transformation model is proposed to convert the obtained emotional behaviors into a specific robot's motion space. To validate the proposed approach, the models were evaluated by two example scenarios of interaction. The experimental results confirmed that the proposed approach endows a social robot with the capability to learn emotional behaviors from individual users, and to generate its emotional bodily expressions. It was also verified that the imitated robot motions are rated emotionally acceptable by the demonstrator and recognizable by the subjects from the same cultural background with the demonstrator.