用于物理人工智能系统故障诊断与闭环修复的本体论驱动世界模型

中文摘要 AI 辅助

EV-WM通过特征分数和事件分数表示候选质量，但这些分数未明确记录未满足的任务谓词、可用修正机制的路由标签或修正后的接受结果。我们提出Onto-EV-WM，这是一种基于本体的诊断和验证门控修正接口，该接口分层于EV-WM之上，而非替代其世界模型架构。已实现的任务局部TBox定义实体类型、谓词签名和约束；特定源的接地将预测或模拟器观测到的状态映射到任务ABoxes；确定性规则在分配路由标签时保留每个缺失的谓词及其参数。学习型或启发式提议者与该符号接口分离；原生任务谓词决定接受情况，且有界协议决定是否重试失败的验证。在对齐的PointMaze评估中，EV-WM和Onto-EV-WM均报告94%的成功率，平均最终状态距离分别为0.90573和0.61177；单独预算的搜索达到100%成功率。在LIBERO-Goal上，本体将失败的任务条件表示为类型化记录，保留其谓词参数，并将其与声明的源/联合修正路由及谓词门控接受关联；完整配置在种子0上报告93.8%的修正窗口成功率，在四个评估采样种子上为94.05±0.30%。在固定的10030任务LIBERO-Plus注册中心上，Onto-EV-WM在8526个任务上成功（85.00%），套件级成功率为LIBERO-10的65.98%、LIBERO-Goal的91.39%以及LIBERO-Object和LIBERO-Spatial的91.38%。这些数字报告了在测试的模拟器协议下，完整的本体驱动配置的性能；本体仅因果份额未单独测量，且未评估真实机器人的恢复情况。

英文摘要

EV-WM represents candidate quality with feature and event scores, but these scores do not explicitly record an unmet task predicate, a route label for an available correction mechanism, or a post-correction acceptance result. We present Onto-EV-WM, an ontology-grounded diagnosis and verification-gated correction interface layered above EV-WM rather than a replacement world-model architecture. The implemented task-local TBox defines entity types, predicate signatures, and constraints; source-specific grounding maps predicted or simulator-observed states to task ABoxes; and deterministic rules retain each missing predicate and its arguments when assigning a route label. Learned or heuristic proposers remain separate from this symbolic interface; native task predicates determine acceptance, and the bounded protocol determines whether a failed verification is retried. In the aligned PointMaze evaluation, EV-WM and Onto-EV-WM both report 94% success, with mean final-state distances of 0.90573 and 0.61177, respectively; the separately budgeted search reaches 100% success. On LIBERO-Goal, the ontology represents failed task conditions as typed records, retains their predicate arguments, and associates them with the declared source/joint correction route and predicate-gated acceptance; the complete configuration reports 93.8% corrected-window success on seed 0 and 94.05 +- 0.30% across four evaluation-sampling seeds. On the fixed 10,030-task LIBERO-Plus registry, Onto-EV-WM succeeds on 8,526 tasks (85.00%), with suite-level success rates of 65.98% for LIBERO-10, 91.39% for LIBERO-Goal, and 91.38% for both LIBERO-Object and LIBERO-Spatial. These numbers report the performance of the complete ontology-grounded configurations under the tested simulator protocols; an ontology-only causal share is not measured separately, and real-robot recovery is not evaluated.

发表机构

• AI Lab, Country Garden Services Group（碧桂园服务集团AI实验室）
• Fudan University（复旦大学）
• Omni AI

机构由 AI 辅助整理，请以论文原文为准。