Journal Paper

For social robots to respond to humans in an appropriate manner they need to use apt affect displays, revealing underlying emotional intelligence. We present an artificial emotional intelligence system for robots, with both a generative and a perceptual aspect. On the generative side, we explore the expressive capabilities of an abstract, faceless, creature-like robot, with very few degrees of freedom, lacking both facial expressions and the complex humanoid design found often in emotionally expressive robots. We validate our system in a series of experiments: in one study, we find an advantage in classification for animated vs static affect expressions and advantages in valence and arousal estimation and personal preference ratings for both animated vs static and physical vs on-screen expressions. In a second experiment, we show that our parametrically generated expression variables correlate with the intended user affect perception. On the perceptual side, we present a new corpus of sentiment-tagged social media posts for training the robot to perceive affect in natural language. In a third experiment we estimate how well the corpus generalizes to an independent data set through a cross validation using a perceptron and demonstrate that the predictive model is comparable to other sentiment-tagged corpi and classifiers. Combining the perceptual and generative systems, we show in a fourth experiment that our automatically generated affect responses cause participants to show signs of increased engagement and enjoyment compared with arbitrarily chosen comparable motion parameters.