Conversational Robot System for Travel Memoir Generation

Reflecting on memories has a positive effect on mental health. For robots that interact with older adults, interactions that look back on memories are also an important application area. Despite the importance of such reminiscence, no study has simultaneously addressed methods to both facilitate robots conversing about users’ memories and generate content from those memories. In this article, we propose TRAVOT, a system that explores the events behind travel photos through conversation and generates travel memoirs with such photos. TRAVOT uses a large language model (LLM) for flexible information collection. Moreover, it can deepen the conversation topic to obtain a more profound story from the user via a topic control mechanism with Meta-LLM. This not only elicits information that cannot be obtained from the photos based on a prepared list of questions but also allows for deepening the discussion by generating additional questions. In addition, it can eliminate redundant questions that result from naive use of an LLM by applying matching judgment with certain required questions. We conducted a user experiment to evaluate TRAVOT’s effectiveness, and we found that the participants could recall more interesting and unusual things that happened during their trips when they had conversations with TRAVOT. The users could also reminisce about their trips when they read the travel memoirs generated by TRAVOT. In addition, TRAVOT increased the amount of information contained in the conversations and travel memoirs.