AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Use

Abstract-- 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. T o 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. I. INTRODUCTION As the development of robot sensing and machine learning technologies accelerates, multivariate time series analysis is becoming more and more critical in the robotics field. Robotic systems usually rely on current time-step sensor data for decision-making and control, which makes it possible to miss the potential temporal patterns over multiple time steps. Additionally, some sensor data, such as force-torque signals, require multiple time steps to capture dynamic behaviors and provide meaningful information.