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

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 pre-recorded 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 preference 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.