用扩散模型检测强力工具使用中的异常，抗噪性强且可实时运行
AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Use
- 基于一步扩散模型提取力矩特征，捕捉复杂时序模式
- 在4个任务上F1和AUROC均优于现有方法，噪声下表现更稳定
- 支持并行评分，适合工业场景的在线异常检测
多变量时间序列异常检测对识别意外事件至关重要，但在强力工具使用场景中面临挑战：真实传感器数据常含噪声、非平稳且因任务与工具而异。为此，我们提出基于扩散模型的AnoF-Diff方法，从时序数据中提取力矩特征并用于异常检测。在4个强力工具使用任务上，与当前先进方法相比，该方法在F1分数和受试者工作特征曲线下面积（AUROC）上表现更优，且对噪声数据更具鲁棒性。我们还提出基于一步扩散的并行异常评分方法，验证了其在多个实际工具使用实验中实现在线异常检测的可行性。
原文摘要 · Abstract (English)
Multivariate time-series anomaly detection, which is critical for identifying unexpected events, has been explored in the field of machine learning for several decades. However, directly applying these methods to data from forceful tool use tasks is challenging because streaming sensor data in the real world tends to be inherently noisy, exhibits non-stationary behavior, and varies across different tasks and tools. To address these challenges, we propose a method, AnoF-Diff, based on the diffusion model to extract force-torque features from time-series data and use force-torque features to detect anomalies. We compare our method with other state-of-the-art methods in terms of F1-score and Area Under the Receiver Operating Characteristic curve (AUROC) on four forceful tool-use tasks, demonstrating that our method has better performance and is more robust to a noisy dataset. We also propose the method of parallel anomaly score evaluation based on one-step diffusion and demonstrate how our method can be used for online anomaly detection in several forceful tool use experiments.
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