Towards an automatic generation of natural gestures for a storyteller robot

Natural gesturing is very important for the credibility of social robots. It is even more crucial for storytelling robots since the expression, emotion and emphasis must be highlighted. In this paper we propose a hybrid gesture generation approach for a storytelling robot that combines beats automatically generated by a GAN with a probabilistic semantic related gesture insertion system. Beats are executed according to a probability based on the duration of the sentences and semantic gesture insertions are dependent of the previous occurrences of the gestures associated to the words. The polarity of the text is extracted and affects several features of the motion to arouse emotion. A qualitative evaluation of robot behavior is conducted and confirms the approach as a promising one as storytelling system.