将虚拟多智能体框架用于真实医疗机器人团队，发现五大失败模式并提出改进方案。
From Virtual Agents to Robot Teams: A Multi-Robot Framework Evaluation in High-Stakes Healthcare Context
- 基于CrewAI框架构建分层机器人团队，在模拟急诊科场景中测试
- 发现角色错位、工具越权等五类持续性故障，影响任务可靠性
- 提出透明流程、主动容错和情境感知三原则，适合医疗机器人研发者
生成模型的发展使多智能体系统（MAS）能够执行写作与代码生成等复杂虚拟任务，但这些能力难以推广至物理世界的多机器人团队。现有框架常将智能体视为抽象任务执行者，忽视空间上下文及机器人实际能力（如感知与导航）等现实约束。为探究此差距，我们在模拟急诊科入职场景中重构并压力测试了基于CrewAI框架的分层多机器人团队。分析揭示五种持续性故障模式：角色错位；工具访问违规；未能及时处理故障报告；未遵守预设工作流程；绕过或虚假上报任务完成。基于此，我们提出三项设计准则：强调流程透明性、主动故障恢复与情境锚定。本研究为构建更稳健的多机器人系统（MARS）提供指导，并指明将虚拟多智能体框架拓展至真实世界的机会。
原文摘要 · Abstract (English)
Advancements in generative models have enabled multi-agent systems (MAS) to perform complex virtual tasks such as writing and code generation, which do not generalize well to physical multi-agent robotic teams. Current frameworks often treat agents as conceptual task executors rather than physically embodied entities, and overlook critical real-world constraints such as spatial context, robotic capabilities (e.g., sensing and navigation). To probe this gap, we reconfigure and stress-test a hierarchical multi-agent robotic team built on the CrewAI framework in a simulated emergency department onboarding scenario. We identify five persistent failure modes: role misalignment; tool access violations; lack of in-time handling of failure reports; noncompliance with prescribed workflows; bypassing or false reporting of task completion. Based on this analysis, we propose three design guidelines emphasizing process transparency, proactive failure recovery, and contextual grounding. Our work informs the development of more resilient and robust multi-agent robotic systems (MARS), including opportunities to extend virtual multi-agent frameworks to the real world.
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