Toward Personalized Tour-Guide Robot: Adaptive Content Planner based on Visitor’s Engagement

Abstract

In the evolving landscape of human-robot interactions, tour-guide robots are increasingly being integrated into various settings. However, the existing paradigm of these robots relies heavily on prerecorded content, which limits effective engagement with visitors. We propose to address this issue of visitor engagement by transforming tour-guide robots into dynamic, adaptable companions that cater to individual visitor needs and preferences. Our primary objective is to enhance visitor engagement during tours through a robotic system capable of assessing and reacting to visitor preferences and engagement. Leveraging this data, the system can calibrate and adapt the tour-guide robot’s content in real-time to meet individual visitor preferences. Through this research, we aim to enhance the tour-guide robots’ impact in delivering engaging and personalized visitor experiences by providing an adaptive tour-guide robot solution that can learn from humans’ preferences and adapt its behaviors by itself.