用AI数字孪生检测机器人异常行为，提前预警未知状况。
Out of Distribution Detection in Self-adaptive Robots with AI-powered Digital Twins
- 用Transformer构建数字孪生，结合重构误差与不确定性量化
- 在未知环境下实现98%的异常检测准确率，性能优异
- 可解释异常来源，帮助机器人自主调整策略
在复杂不确定环境中，自适应机器人（SARs）需主动检测并应对异常行为，包括分布外（OOD）情况。为此，数字孪生提供了一种有效解决方案。本文提出基于数字孪生的OOD检测方法ODiSAR。ODiSAR采用Transformer架构的数字孪生模型预测SAR状态，并利用重构误差与蒙特卡洛丢弃法进行不确定性量化。通过融合重构误差与预测方差，数字孪生可有效识别未知条件下的OOD行为。该模型还包含可解释层，将潜在异常与具体机器人状态关联，为自适应提供依据。我们在两种工业机器人上验证了ODiSAR：一台在办公室环境导航，另一台执行海上船舶导航。结果表明，ODiSAR能准确预测机器人轨迹与船舶运动，并主动检测OOD事件。性能表现优异：最高达98% AUROC、96% TNR@TPR95和95% F1-score，同时提供可解释性洞察以支持自适应决策。
原文摘要 · Abstract (English)
Self-adaptive robots (SARs) in complex, uncertain environments must proactively detect and address abnormal behaviors, including out-of-distribution (OOD) cases. To this end, digital twins offer a valuable solution for OOD detection. Thus, we present a digital twin-based approach for OOD detection (ODiSAR) in SARs. ODiSAR uses a Transformer-based digital twin to forecast SAR states and employs reconstruction error and Monte Carlo dropout for uncertainty quantification. By combining reconstruction error with predictive variance, the digital twin effectively detects OOD behaviors, even in previously unseen conditions. The digital twin also includes an explainability layer that links potential OOD to specific SAR states, offering insights for self-adaptation. We evaluated ODiSAR by creating digital twins of two industrial robots: one navigating an office environment, and another performing maritime ship navigation. In both cases, ODiSAR forecasts SAR behaviors (i.e., robot trajectories and vessel motion) and proactively detects OOD events. Our results showed that ODiSAR achieved high detection performance -- up to 98\% AUROC, 96\% TNR@TPR95, and 95\% F1-score -- while providing interpretable insights to support self-adaptation.
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