Affective Robotics: Modelling and Testing Cultural Prototypes

Abstract

If robots are to successfully interact with humans, they need to measure, quantify and respond to the emotions we produce. Similar to humans, the perceptual cue inputs to any modelling that allows this will be based on behavioural expression and body activity features that are prototypical of each emotion. However, the likely employment of such robots in different cultures necessitates the tuning of the emotion feature recognition system to the specific feature profiles present in these cultures. The amount of tuning depends on the relative convergence of the cross-cultural mappings between the emotion feature profiles of the cultures where the robots will be used. The GRID instrument and the cognitive corpus linguistics methodology were used in a contrastive study analysing a selection of behavioural expression and body activity features to compare the feature profiles of joy, sadness, fear and anger within and between Polish and British English. The intra-linguistic differences that were found in the profile of emotion features suggest that weightings based on this profile can be used in robotic modelling to create emotion-sensitive socially interacting robots. Our cross-cultural results further indicate that this profile of features needs to be tuned in robots to make them emotionally competent in different cultures.