"Once Upon a Time…": an Adaptive Robotic Behavior for Engaging Cooperative Storytelling

Assistive robots can be valuable conversational partners for cooperative tasks, such as storytelling, fostering creativity and social bonding. Through the use of foundational models, such as LLMs, robots can more effectively and naturally generate story narrations that are enjoyed by humans. In such a scenario, however, it is fundamental to consider the users’ feedback and reactions to adapt the story and the interaction in a way that actively sustains their interest. In this work, we propose an LLM-assisted storytelling generation method that employs different robot’s communication modalities to stimulate the user’s behavioral, affective, and cognitive engagement during the interaction and affect the narration of the story. Moreover, we investigated the introduction of an adaptive interaction policy to choose the most suitable actions based on the user’s observed engagement. We conducted a user study with 36 participants to assess our proposed approach, and demonstrated that it manages to effectively assist participants in an engaging way, with the robot being perceived as friendly and trustworthy. Moreover, the policy adaptation results in a perception of the robot with a higher arousal while a more interactive approach led to a better perceived social intelligence.