MICA让工厂多智能体协作更实时可靠，支持离线部署。
MICA: Multi-Agent Industrial Coordination Assistant
- 五类专用语言智能体协同，语音交互实现动态调整。
- 自适应步融合提升步骤理解准确率，响应速度更快。
- 专为工业场景设计，适合制造、维护等隐私敏感任务。
工业流程需要在计算资源有限、网络受限且隐私要求严格的条件下，提供灵活可信的辅助。本文提出MICA（多智能体工业协作助手），一个基于感知与语音交互的系统，可实时提供装配、故障排查、零件查询和维护指导。MICA协调五个角色特化的语言智能体，并由安全检查器审核，确保输出准确合规。为实现鲁棒的步骤理解，提出自适应步融合（ASF），动态融合专家推理与自然语音反馈的在线适应。此外，构建了涵盖典型任务类别的多智能体协作新基准，并提出针对工业辅助的评估指标，支持不同协作架构的系统比较。实验表明，MICA在任务成功率、可靠性与响应性上均优于基线结构，且可在实际离线硬件上部署。这些贡献推动了可部署、隐私保护的多智能体助手在动态工厂环境中的落地。代码将公开于 https://github.com/Kratos-Wen/MICA。
原文摘要 · Abstract (English)
Industrial workflows demand adaptive and trustworthy assistance that can operate under limited computing, connectivity, and strict privacy constraints. In this work, we present MICA (Multi-Agent Industrial Coordination Assistant), a perception-grounded and speech-interactive system that delivers real-time guidance for assembly, troubleshooting, part queries, and maintenance. MICA coordinates five role-specialized language agents, audited by a safety checker, to ensure accurate and compliant support. To achieve robust step understanding, we introduce Adaptive Step Fusion (ASF), which dynamically blends expert reasoning with online adaptation from natural speech feedback. Furthermore, we establish a new multi-agent coordination benchmark across representative task categories and propose evaluation metrics tailored to industrial assistance, enabling systematic comparison of different coordination topologies. Our experiments demonstrate that MICA consistently improves task success, reliability, and responsiveness over baseline structures, while remaining deployable on practical offline hardware. Together, these contributions highlight MICA as a step toward deployable, privacy-preserving multi-agent assistants for dynamic factory environments. The source code will be made publicly available at https://github.com/Kratos-Wen/MICA.
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