PACE：通过人机交互中的对话启发实现角色适应
PACE: Persona Adaptation through Conversational Elicitation in Human-Robot Interaction
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中文总结 AI 辅助
研究如何让类人机器人有可适应角色，提出PACE框架，通过用户问答动态生成角色，经提高结构化提示，经系统集成转化为行为，实证评估显示其对人机交互多方面有积极影响，为部署个性化等身份开辟途径。
中文摘要 AI 辅助
为类人机器人配备连贯且可适应的角色对于促进自然、引人入胜且值得信赖的人机交互至关重要。然而，现有方法往往依赖缺乏灵活性的静态、硬编码身份。本文提出PACE，一种在Ameca类人机器人上交互式生成和部署结构化角色的新框架。系统引入交互式角色启发管道，通过用户问答动态合成定制且基于心理的身份。该启发过程进入角色提示编译阶段，生成基于多视角维度的结构化角色提示。详细说明了将此结构化规范转化为富有表现力的多模态类人行为所需的实体系统集成。通过全面的人机交互实证评估，与通用基线相比，评估了动态生成角色对用户信任、感知拟人化、角色一致性、个人相关性和交互质量的影响。这些贡献为在实体类人助手部署个性化、交互式和可靠身份建立了可扩展途径。
英文摘要
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/
发表机构
- Macquarie University（麦考瑞大学）
- NVIDIA（英伟达）
机构由 AI 辅助整理，请以论文原文为准。
