发表机构
Karlsruhe Institute of Technology; Hunan University（卡尔斯鲁厄理工学院; 湖南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出HACT过渡模型，通过角色保持历史和标记时间点过程建模双手事件，实现程序性异常检测，在双手电动工具程序上取得最优AUPRC和F1，且无需重训即可泛化。
AI 中文摘要
双手装配中的程序性异常检测需要根据迄今为止的执行情况来判断每只手的行为。纠正性动作在孤立情况下可能看起来异常，而视觉上合理的行为可能违反程序的顺序。我们提出了HACT，一种基于预测的每只手事件的过渡模型。角色保持历史记录保持双手的并发职责，标记的时间点过程为每个观察到的过渡分配语义和时间上的意外度。监督证据头和一个两状态滤波器将这些意外度转换为每只手的异常后验概率。对预测事件和参与者不重叠折的恢复感知协议，在验证参与者上选择的操作点报告恢复误报率。在两个双手电动工具程序上，HACT在比较方法中具有最高的AUPRC和F1，并且恢复警报最少。在不重新训练的情况下应用于同一产品的不同装配顺序，它保持最高的AUPRC和F1。源代码可在该https URL获取。
英文摘要
Procedural anomaly detection in bimanual assembly requires judging each hand action against the execution so far. A corrective action may look unusual in isolation, while a visually plausible action can violate the order of the procedure. We present HACT, a transition model over predicted per-hand events. A role-preserving history keeps the concurrent responsibilities of both hands, and a marked temporal point process assigns each observed transition a semantic and temporal surprisal. A supervised evidence head and a two-state filter convert these surprisals into per-hand anomaly posteriors. A recovery-aware protocol on predicted events and participant-disjoint folds reports the recovery false-positive rate at an operating point selected on validation participants. On two bimanual power-tool procedures HACT has the highest AUPRC and F1 among the compared methods and the fewest recovery alarms. Applied without retraining to a different assembly order of the same product, it retains the highest AUPRC and F1. The source code is available at https://github.com/Kratos-Wen/HACT.
Comments6 pages, 1 figure, 3 tables. Code: https://github.com/Kratos-Wen/HACT
