  Communication in both human-human and human-robot interac-tion (HRI) contexts
consists of verbal (speech-based) and non-verbal(facial expressions, eye gaze,
gesture, body pose, etc.) components.The verbal component contains semantic and
affective information;accordingly, HRI work on the gesture component so far has
focusedon rule-based (mapping words to gestures) and data-driven
(deep-learning) approaches to generating speech-paired gestures basedon either
semantics or the affective state. Consequently, most ges-ture systems are
confined to producing either semantically-linkedor affect-based gesticures.
This paper introduces an approach forenabling human-robot communication based
on a theory-drivenapproach to generate speech-paired robot gestures using both
se-mantic and affective information. Our model takes as input textand sentiment
analysis, and generates robot gestures in terms oftheir shape, intensity, and
speed.
