Adaptive Interaction Style Modulation via Reinforcement Learning and Prompted Language Generation

In the context of socially assistive robotics, there is a growing need for interaction strategies that can adapt to users' emotional states in real time, as fixed or generic communication styles often fail to sustain user engagement or meet individual motivational needs, especially in long-term human-robot interaction. To address this challenge, this paper presents a novel framework for adaptive interaction style modulation in socially assistive agents, combining large language models (LLMs) with reinforcement learning based on real-time emotion recognition. The proposed architecture leverages multimodal sensing to monitor the user's affective state and dynamically selects among predefined communicative styles using Thompson Sampling. At each interaction turn, the user's emotional feedback is converted into a scalar reward, allowing the system to reinforce styles that yield more positive affective outcomes. Style conditioning is operationalized through prompting strategies that guide the LLM to generate responses aligned with the selected tone. A preliminary evaluation using VADER sentiment analysis demonstrates that stylistic prompts successfully induce measurable differences in sentiment polarity, neutrality, and verbosity. These findings suggest the viability of our approach to style-aware dialogue generation and support its potential for long-term adaptation in personalized human-agent interaction.