Evaluation of Robot Emotion Expressions for Human-Robot Interaction

Emotion recognition has fostered more suitable and effective human-robot interaction (HRI). In particular, social robots have to imitate the expression of feeling through their voices and body gestures in order to ameliorate this interaction. However, robot's hardware limitations (few joints and computational resources) may restrict the quality of robot's expressions. To contribute to this area, we conducted a study on how emotions are expressed by humans through gestures, body language, and movements. This study allows understanding universal representation of emotions (movements and gestures) and designing similar movements for robots, despite their hardware limitations. Based on that, we develop and evaluate an emotional interaction system for robots, specifically for Pepper robot. This system utilizes verbal emotion recognition, based on deep learning techniques to interpret and respond with movements and emojis, thus enriching the dynamics of HRI. We implemented two versions of such as interaction system: on board implementation (the emotion recognition process is executed by the robot) and a server-based implementation (the emotion recognition is performed by an external server connected to the robot). We assessed the performance of both versions, as well as the acceptance of robot expressions for HRI. Results show that the combined use of emotional movements and emojis by robot significantly improves the accuracy of emotional conveyance.