mlsanityData to 14 Oct 2026

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

arXiv 2509.15153 ↗First posted 18 Sep 2025cs.RO cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 37 references that are in the map. First posted on arXiv 18 Sep 2025.

Builds on

25 of 37 references.

References · 25

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,214
  3. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  4. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 931
  5. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  6. 2016Learning Temporal Regularity in Video Sequencescited by 254
  7. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 215
  8. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  9. 2020A review on outlier/anomaly detection in time series datacited by 189
  10. 2018A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Datacited by 182
  11. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 131
  12. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
13 more
  1. 2023On Diffusion Modeling for Anomaly Detectioncited by 72
  2. 2024A Survey on Diffusion Models for Time Series and Spatio-Temporal Datacited by 67
  3. 2023ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detectioncited by 40
  4. 2017Deep Reinforcement Learning for High Precision Assembly Taskscited by 39
  5. 2024FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulationcited by 33
  6. 2024Dive into Time-Series Anomaly Detection: A Decade Reviewcited by 24
  7. 2024FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertaintycited by 23
  8. 2024TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulationcited by 23
  9. 2023Time Series Anomaly Detection using Diffusion-based Modelscited by 12
  10. 2025Robotic Compliant Object Prying Using Diffusion Policy Guided by Vision and Force Observationscited by 10
  11. 2020A Learning Approach to Robot-Agnostic Force-Guided High Precision Assemblycited by 6
  12. 2019Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoderscited by 5
  13. 2024Ensuring Force Safety in Vision-Guided Robotic Manipulation via Implicit Tactile Calibrationcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learningcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026FirstDiff: One-Step Diffusion-Based Anomaly Detection for Multivariate Time Series via Initial Noise Prediction
  2. 2025Research on Anomaly Detection Methods Based on Diffusion Models
  3. 2025Anomaly detection for generic failure monitoring in robotic assembly, screwing and manipulation ↗
  4. 2025Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series
  5. 2023Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models
  6. 2024Anomaly detection using Diffusion-based methods
  7. 2023LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection
  8. 2025Anomaly Detection and Generation with Diffusion Models: A Survey