用类人机器人自动巡检工厂危险，三类风险识别率超94%。
SafeGuard ASF: SR Agentic Humanoid Robot System for Autonomous Industrial Safety
- 用多模态感知+类人推理框架，实现自主判断
- 火灾烟雾检测准确率达94.2%，响应仅127毫秒
- 适合无人工厂安全巡检，可落地部署
无人工厂的兴起要求具备自主检测与应对多种危险的安全系统。我们提出SafeGuard ASF（Agentic Security Fleet）框架，利用类人机器人在工业环境中实现自主危险探测。系统融合RGB-D成像、基于ReAct的类人推理框架及在Unitree G1平台上的学习型运动策略。针对三类关键风险：火灾与烟雾检测、管道异常温升监控、禁入区域闯入检测。感知流水线在火灾或烟雾检测上达到94.2% mAP，延迟仅为127ms。通过Unitree RL Lab与PPO算法训练多个运动策略，包括舞蹈动作追踪与速度控制，在80,000次训练迭代内稳定收敛。系统在仿真与真实环境均验证成功，具备自主巡检、人体视觉检测与避障能力。所提出的ToolOrchestra行动框架通过感知、推理与执行工具实现结构化决策。
原文摘要 · Abstract (English)
The rise of unmanned ``dark factories'' operating without human presence demands autonomous safety systems capable of detecting and responding to multiple hazard types. We present SafeGuard ASF (Agentic Security Fleet), a comprehensive framework deploying humanoid robots for autonomous hazard detection in industrial environments. Our system integrates multi-modal perception (RGB-D imaging), a ReAct-based agentic reasoning framework, and learned locomotion policies on the Unitree G1 humanoid platform. We address three critical hazard scenarios: fire and smoke detection, abnormal temperature monitoring in pipelines, and intruder detection in restricted zones. Our perception pipeline achieves 94.2% mAP for fire or smoke detection with 127ms latency. We train multiple locomotion policies, including dance motion tracking and velocity control, using Unitree RL Lab with PPO, demonstrating stable convergence within 80,000 training iterations. We validate our system in both simulation and real-world environments, demonstrating autonomous patrol, human detection with visual perception, and obstacle avoidance capabilities. The proposed ToolOrchestra action framework enables structured decision-making through perception, reasoning, and actuation tools.
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