发表机构
AI Lab, Country Garden Services; Omni AI; East China Normal University（碧桂园服务AI实验室; Omni AI; 华东师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出统一具身智能体运行时HROS及其住宅巡检实现Argos，集成感知、规划与多模态分析，实验验证了高可达性、低延迟和可靠闭环巡检。
AI 中文摘要
自主物业巡检需要的不仅仅是稳健的机器人导航：一个可部署的系统必须在可追溯的操作闭环中连接异构感知、可复用的自主能力、多模态场景理解、人机交互和企业响应。现有的四足巡检系统通常通过特定任务的接口集成这些功能，这使得上下文协调、知识复用和受控适应变得困难。本文提出了Harness Robotic OS（HROS），一个统一的具身智能体运行时，以及Argos，其在住宅社区巡检中的实现。HROS将系统组织为机器人运行时、具身自主技能、认知智能体运行时以及交互与操作平面。一个共享上下文将物理状态与智能体推理连接起来；流式ASR/TTS支持基于语音的任务交互；分层的工作记忆、情景记忆和语义记忆保存操作知识；一个安全门控的自我进化循环将执行轨迹转换为带版本管理的候选更新，而不允许不受约束的在线修改。Argos原型集成了Vbot四足机器人、Fast-LIO2定位与建图、Hobot-Stereo深度感知、PCT-Planner全局规划、EGO-Planner局部运动生成，以及OpenClaw编排的Qwen3-VL巡检分析。在住宅物业环境中的实验实现了100%的航点可达性、低于10厘米的室外定位误差、低于200毫秒的局部障碍响应延迟、85%至95%的代表性危险检测率，以及99%的警报传递和结构化报告生成成功率。这些结果验证了已部署的导航与巡检闭环，而HROS为记忆增强、语音感知和持续可改进的具身巡检智能体提供了可扩展的软件基础。
英文摘要
Autonomous property inspection requires more than robust robot navigation: a deployable system must connect heterogeneous sensing, reusable autonomy capabilities, multimodal scene understanding, human interaction, and enterprise response within a traceable operational loop. Existing quadruped inspection systems commonly integrate these functions through task-specific interfaces, making contextual coordination, knowledge reuse, and controlled adaptation difficult. This paper presents \textit{Harness Robotic OS} (HROS), a unified embodied-agent runtime, and Argos, its realization for residential-community inspection. HROS organizes the system into robot runtime, embodied autonomy skills, cognitive agent runtime, and interaction and operations planes. A shared context connects physical state with agent reasoning; streaming ASR/TTS supports voice-based mission interaction; hierarchical working, episodic, and semantic memory preserves operational knowledge; and a safety-gated self-evolution loop converts execution traces into versioned candidate updates without permitting unconstrained online modification. The Argos prototype integrates a Vbot quadruped, Fast-LIO2 localization and mapping, Hobot-Stereo depth perception, PCT-Planner global planning, EGO-Planner local motion generation, and OpenClaw-orchestrated Qwen3-VL inspection analysis. Experiments in a residential property environment achieved 100\% waypoint reachability, outdoor localization error below 10~cm, local obstacle-response latency below 200~ms, representative hazard-detection rates of 85--95\%, and 99\% success in alarm delivery and structured-report generation. These results validate the deployed navigation and inspection closed loop, while HROS provides an extensible software foundation for memory-augmented, voice-aware, and continuously improvable embodied inspection agents.
