让机器人各有个性并智能协作，提升人机互动自然度
M2HRI: An LLM-Driven Multimodal Multi-Agent Framework for Personalized Human-Robot Interaction
- 给每个机器人赋予人格和长期记忆，形成独特身份
- 用户实测显示个性可辨，记忆与协作机制显著提升交互自然度
- 适合研究个性化人机交互与多机器人系统设计的团队
多机器人系统在家庭和医院等社交场景中潜力巨大，但现有系统常将机器人视为功能等同的个体，忽视了不同代理身份如何影响用户感知，以及这种个体性对多机器人协作需求的影响。为此，我们提出M2HRI，一种基于大模型的多模态多代理框架，通过人格特征和长期记忆为每个机器人赋予身份，并引入情境化协调机制调控代理参与。在一项控制实验（n=105）中，我们发现大多数人格差异具有可区分性且稳定表达；长期记忆提升了用户偏好感知与交互自然度，情境化协调则改善了对话流畅性、回应恰当性及重叠规避能力。结果表明，代理个体性与情境化参与协调在支持连贯且社会适宜的多代理人机交互中起互补作用。
原文摘要 · Abstract (English)
Multi-robot systems hold significant promise for social environments such as homes and hospitals, yet existing multi-robot systems often treat robots as functionally interchangeable, overlooking how distinct agent identities shape user perception and how such individuality changes the coordination requirements of multi-robot interaction. To address this, we introduce M2HRI, a multimodal multi-agent framework that models each robot as an identity-bearing agent through personality and long-term memory, together with a contextualized coordination mechanism that regulates agent participation. In a controlled user study (n = 105) in a multi-agent human-robot interaction (HRI) scenario, we found that most personality contrasts were distinguishable and consistently expressed. Long-term memory improved preference awareness and interaction naturalness, while contextualized coordination improved conversational flow, response appropriateness, and overlap avoidance. Together, these findings show that agent individuality and contextualized participation coordination play complementary roles in supporting coherent and socially appropriate multi-agent HRI. Project website available at https://project-m2hri.github.io/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。