Towards Personalized Social Robots: Adaptive Prompting for Real-Time Context-Aware Conversations

Abstract

Social robots have demonstrated great potential in various domains. Recent advancements in Large Language Models (LLMs) have expanded the conversational capabilities of these robots, enabling more personalized user interactions. However, current systems primarily focus on behavior or task personalization, or they require extensive pre-training and fine-tuning to achieve language personalization. This paper introduces adaptive prompting, a formal framework for real-time linguistic personalization in LLM-driven robots. By structuring interaction as a sequence of interdependent prompts, adaptive prompting enables controllable, efficient, and scalable personalization without additional model training. To validate our approach, we present a system that integrates adaptive prompting in a social robot to dynamically adapt to user attributes and preferences to provide personalized productivity coaching for college students with Attention Deficit Hyperactivity Disorder (ADHD). Our findings demonstrate that personalized coaching via adaptive prompting improves user engagement and overall coaching effectiveness compared to non-personalized coaching. This indicates the effectiveness of the proposed approach for user adaptation and personalization in social robots, particularly in the aforementioned contexts.