BargainingNAO: Exploring the Effects of Robot Expertise and Embodiment in Human-Robot Negotiation

Abstract

As robots become increasingly integrated into human environments, their ability to perform strategic, socially grounded tasks, such as negotiation, becomes critical. Recent advancements in large language models (LLMs) have enabled robotic agents to exhibit fluent, human-like dialogue in many contexts. However, general-purpose LLMs often lack the domain-specific expertise and robust strategic reasoning required for effective human-like negotiation. Moreover, it remains unclear how the agent's expertise and physical embodiment jointly influence negotiation performance and user perception. To address these limitations, we develop an expert embodied negotiation framework that extracts linguistic and emotional features from human speech and facial cues, and uses these features together with domain knowledge retrieved via a retrieval-augmented generation (RAG) process to guide a large language model to generate synchronized verbal responses and select persuasive gestures for closed-loop human-robot negotiation. Using this framework, we instantiate three agent conditions with varying levels of embodiment and expertise: a non-expert embodied agent, an expert voice-only agent, and an expert embodied agent. We evaluate these agents in a withinsubject study $(n=30)$ using a human-robot negotiation task to investigate how agent expertise and embodiment influence negotiation performance and user perception. Results indicate that embodiment is associated with higher perceived rapport and interaction clarity, whereas expertise tends to support more structured and strategic negotiation behavior. These findings suggest that integrating embodiment and domain expertise holds promise for designing robotic agents capable of more effective and socially grounded human-robot negotiation.