将实验室语言规则转化为可运行的安全监控,提升智能实验系统安全性。
LabGuard: Grounding Natural-Language Laboratory Rules into Runtime Guards for Embodied Laboratory Agents

- 将自然语言规则转换为机器可执行的规范
- 减少安全事件率至23.8%,任务完成率保持79.4%的F1值
- 适合需要高安全性的自主实验机器人开发人员
科学类具身代理在执行实验流程方面日益成熟,但在动态实验环境中安全执行仍具挑战。现有安全方法常忽略将实验室自然语言(如安全规则、手册、规程)转化为可机器验证的运行时约束这一中间步骤。本文提出LabGuard(实验室守护者),一个从语言到执行的安全套件,将自然语言实验室规则转化为可执行规范,并部署为运行时守护机制。其包含三个核心组件:LabGuard-IR,定义类型化的可执行表示;LabGuard-Bench,提供由203条种子规则扩展出的812个标注样本;LabGuard-Grounder,将自然语言规则映射至LabGuard-IR。生成的IR实例经由LabGuard Pipeline编译为运行时监控器,并在控制器边界应用。实验表明,LabGuard可泛化至未见过的规则来源,实现79.4的任务范围F1,将不安全事件从39.5%降至23.8%。在LabUtopia中,其运行时监控与ACT集成,干预率低于0.5%的同时维持任务成功率。
原文摘要 · Abstract (English)
Scientific embodied agents are increasingly capable of carrying out laboratory procedures, but executing these procedures safely in dynamic laboratory environments remains challenging. Current safety approaches often overlook the intermediate step of transforming laboratory natural language, including safety rules, manuals, protocols, and standard operating procedures, into machine-checkable runtime constraints. We introduce LabGuard (Laboratory Guard), a language-to-execution safety suite that grounds natural-language laboratory rules into executable specifications and deploys them as runtime guards. LabGuard includes three core components: LabGuard-IR, which defines a typed executable representation; LabGuard-Bench, which provides 812 supervised annotations expanded from 203 seed laboratory rules; and LabGuard-Grounder, which maps natural-language laboratory rules into LabGuard-IR. The resulting IR instances are handled by the LabGuard Pipeline, which compiles them into runtime monitors and applies them at the controller boundary. Experiments show that LabGuard generalizes to unseen laboratory-rule sources, achieves 79.4 task-scope F1, and reduces unsafe events from 39.5% to 23.8% after monitor compilation. In LabUtopia, its runtime monitors integrate with ACT, keeping interventions below 0.5% while preserving task success.
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