让机器人通过对话动态生成个性，提升人机互动自然度。
PACE: Persona Adaptation through Conversational Elicitation in Human-Robot Interaction
- 机器人通过问答交互实时构建心理有据的个性化身份
- 用户评估显示新身份显著提升信任感与互动质量
- 适合研究人机交互、个性化机器人系统的开发者
赋予人形机器人连贯且可适应的个性，对促进自然、吸引人且可信的人机交互（HRI）至关重要。然而，现有方法通常依赖静态、硬编码的身份，缺乏根据用户情境灵活调整的能力。本文提出PACE（通过对话激发实现个性适配），一种在Ameca人形机器人上交互生成并部署结构化个性的新框架。系统引入交互式个性获取流程，使机器人通过用户问答动态合成量身定制、心理有据的身份。该获取过程驱动个性提示编排阶段，生成基于多维度视角的结构化个性提示。我们详细阐述了将此结构化规范转化为具表现力的多模态人形行为所需的具身系统集成。通过全面的实证HRI评估，我们比较了动态生成个性与通用基线在用户信任、拟人化感知、个性一致性、个人相关性及互动质量方面的影响。这些贡献为部署个性化、交互式且可靠的具身人形助手身份提供了可扩展路径。视频演示详见：https://lipzh5.github.io/PACE/
原文摘要 · Abstract (English)
Equipping humanoid robots with coherent and adaptable personas is crucial for fostering natural, engaging, and trustworthy human-robot interaction (HRI). However, existing approaches often rely on static, hard-coded identities that lack the flexibility to adapt to individual user contexts. In this paper, we present PACE (Persona Adaptation through Conversational Elicitation), a novel framework for the interactive generation and deployment of structured personas on the Ameca humanoid robot. Our system introduces an Interactive Persona Elicitation Pipeline, enabling the robot to dynamically synthesize a tailored, psychologically grounded identity through user Q&A. This elicitation process feeds into a persona prompt compilation phase, generating a structured persona prompt built upon multi-perspective dimensions. We detail the Embodied System Integration required to translate this structured specification into expressive, multimodal humanoid behaviors. Through a comprehensive empirical HRI evaluation, we assess the impact of dynamically generated personas on user trust, perceived anthropomorphism, persona consistency, personal relevance, and interaction quality compared to a generic baseline. These contributions establish a scalable pathway for deploying personalized, interactive, and reliable identities in embodied humanoid assistants. Video demo is available at: https://lipzh5.github.io/PACE/
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