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

2025-09-18

AI Summary

Real-time anomaly detection in force-controlled tool operations is challenging due to strong noise, non-stationarity, and high inter-task variability in multivariate time-series sensor data. Method: This paper proposes a one-stage diffusion model for real-time anomaly detection. It employs a parallel diffusion mechanism to directly model the distribution of time-series features—bypassing error-prone multi-step denoising—and incorporates implicit representation learning of torque sequences to yield efficient, interpretable anomaly scores. Results: Evaluated on four real-world force-control tasks, the method achieves statistically significant improvements in F1-score and AUROC over state-of-the-art approaches. It demonstrates superior robustness under heavy noise and low signal-to-noise ratios, strong stability across distribution shifts, and effective online adaptability—validating its practicality for safety-critical robotic manipulation.

Technology Category

• Intelligent Robots: Manipulation
• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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. 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.

Problem

• Detects anomalies in multivariate time-series forceful tool use
• Addresses noisy non-stationary sensor data variations
• Enables online anomaly detection through diffusion models

Innovation

• Diffusion model for force-torque feature extraction
• One-step parallel anomaly score evaluation
• Online anomaly detection for noisy datasets