发表机构
Prairie View A&M University; Texas A&M University（草原景观农业机械大学; 德克萨斯A&M大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究揭示语言机器人系统中安全监控器基于表面计划判定与图细化轨迹判定存在差异，提出轨迹细化作为轻量级缓解与诊断工具。
AI 中文摘要
语言使能的机器人系统日益将语义图规划与时间逻辑安全监控器相结合。我们研究了这些系统中的轨迹完整性假设：监控器检查的高层动作序列是否代表了执行过程中诱导的导航和隐式动作效果。我们在RoboGuard中审计了这一假设，通过比较其在表面计划上的判定与在同一线性时间逻辑（LTL）规范下图细化轨迹上的判定。我们的评估包括28个受控案例，涵盖五个动作抽象家族，以及14个端到端案例，其中SPINE [1]从自然语言指令生成计划，而RoboGuard生成场景基础的安全规范。在受控评估中，所有12个目标抽象案例均表现出预测的表面与细化差异，而所有16个对照组行为符合预期，这促使基于图的轨迹细化作为物理AI安全监控器的轻量级缓解措施和诊断工具。
英文摘要
Language-enabled robot systems increasingly combine semantic-graph planning with temporal-logic safety monitors. We investigate a trace-completeness assumption in these systems: whether the high-level action sequence checked by a monitor represents the navigation and implicit action effects induced during execution. We audit this assumption in RoboGuard by comparing its verdict on a surface plan with its verdict on a graph-refined trace under the same Linear Temporal Logic (LTL) specification. Our evaluation comprises 28 controlled cases spanning five action-abstraction families and 14 end-to-end cases in which SPINE [1] generates plans from natural-language instructions while RoboGuard generates scene-grounded safety specifications. In the controlled evaluation, all 12 targeted abstraction cases exhibit the predicted surface-versus-refined discrepancy while all 16 controls behave as expected, motivating graph-based trace refinement as a lightweight mitigation and a diagnostic tool for physical-AI safety monitors.
Comments5 pages, 1 figure
