Autonomous Storytelling for Social Robot with Human-Centered Reinforcement Learning

Social robots are gradually integrating into human’s daily lives. Storytelling by social robots could bring a different experience to users through non-verbal and emotional capabilities compared to text-only one. However, as user needs and preferences over storytelling might change over time during long-term interaction with social robots, it is important for social robots to learn from social interactions with human users in real-time. In this paper, we propose to allow our social robot Haru to learn personalized storytelling styles for different human user’s emotional states via human-centered reinforcement learning using the reward provided and delivered by directly interaction with the user explicitly. Results of our user study show that Haru can learn to adapt its storytelling style for detected human emotional states in a few number of interactions, and was perceived to have a better storytelling performance, experience and impact than a neutral one.