Personalized Storytelling with Social Robot Haru

Hui Wang, Lei Zhang, Chuanxiong Zheng, Randy Gomez, Keisuke Nakamura, Guangliang Li

Abstract: In previous studies of applying storytelling to robotics, the emotions and actions of robots are usually pre-determined, resulting in a homogeneous storytelling style for the robot. In this paper, we propose an empathic and adaptive framework for robot’s storytelling that facilitates social robot Haru to learn from human teachers. In this framework, the robot Haru performs empathic storytelling based on the human teacher’s voice, and then changes its narrative styles (e.g., featured by pitch, emotion, action, etc.) to capture the listener’s attention. The whole experiment was conducted on social robot Haru. Haru’s communicative modality involves face and body movements, sound voice and non-verbal sound, which have great potentials for storytelling. The affective robot for storytelling was compared to a neural one and human teachers. Preliminary results show the social robot for storytelling can make use of human teachers as integral to the design of the system and provide a personalized storytelling experience. Moreover, participants had positive attitudes toward storytelling by an affective robot compared to a neutral one.