World Model Failure Classification and Anomaly Detection for Autonomous Inspection

• URL: http://arxiv.org/abs/2602.16182v1
• Date: Wed, 18 Feb 2026 04:41:14 GMT
• Title: World Model Failure Classification and Anomaly Detection for Autonomous Inspection
• Abstract summary: We propose a hybrid framework that combines supervised failure classification with anomaly detection.<n>Our approach uses a world model backbone with compressed video inputs.<n>Experiments show over 90% accuracy in distinguishing between successes, failures, and OOD cases.
• Score: 23.48742973289626
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous inspection robots for monitoring industrial sites can reduce costs and risks associated with human-led inspection. However, accurate readings can be challenging due to occlusions, limited viewpoints, or unexpected environmental conditions. We propose a hybrid framework that combines supervised failure classification with anomaly detection, enabling classification of inspection tasks as a success, known failure, or anomaly (i.e., out-of-distribution) case. Our approach uses a world model backbone with compressed video inputs. This policy-agnostic, distribution-free framework determines classifications based on two decision functions set by conformal prediction (CP) thresholds before a human observer does. We evaluate the framework on gauge inspection feeds collected from office and industrial sites and demonstrate real-time deployment on a Boston Dynamics Spot. Experiments show over 90% accuracy in distinguishing between successes, failures, and OOD cases, with classifications occurring earlier than a human observer. These results highlight the potential for robust, anticipatory failure detection in autonomous inspection tasks or as a feedback signal for model training to assess and improve the quality of training data. Project website: https://autoinspection-classification.github.io

Related papers

• Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latents [58.49879338545782]
  Long-horizon tasks are common in real-world robotic deployments, yet failure detection for such tasks remains underexplored.<n>We present Foresight, a failure detection framework that monitors manipulation trajectories using latent representations from an action-conditioned world model.<n>Our results suggest that action-conditioned world-model embeddings provide a scalable representation for reliable failure monitoring in long-horizon manipulation.
  arXiv  Detail & Related papers  (2026-06-22T09:32:28Z)
• CIAware-Bench: Benchmarking Control Intervention Awareness Across Frontier LLMs [100.38986535324284]
  We introduce textbfCIAware-Bench, a benchmark for measuring textbfcontrol textbfintervention (CI) awareness across frontier models.<n>CIAware-Bench tests whether models can distinguish their own trajectories from those modified by a control intervention.
  arXiv  Detail & Related papers  (2026-06-09T16:24:16Z)
• ProbeLLM: Automating Principled Diagnosis of LLM Failures [89.44131968886184]
  We propose ProbeLLM, a benchmark-agnostic automated probing framework that elevates weakness discovery from individual failures to structured failure modes.<n>By restricting probing to verifiable test cases and leveraging tool-augmented generation and verification, ProbeLLM grounds failure discovery in reliable evidence.
  arXiv  Detail & Related papers  (2026-02-13T14:33:13Z)
• Unsupervised Learning for Industrial Defect Detection: A Case Study on Shearographic Data [0.0]
  This study explores unsupervised learning methods for automated anomaly detection in shearographic images.<n>Three architectures are evaluated: a fully connected autoencoder, a convolutional autoencoder, and a student-teacher model.<n>Results show that the student-teacher approach achieves superior classification and enables precise localization.
  arXiv  Detail & Related papers  (2025-11-04T12:48:02Z)
• Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization [0.28647133890966986]
  Unsupervised anomaly detection aims to detect defective parts of a sample by having access, during training, to a set of normal, i.e. defect-free, data.<n>We propose a novel UAD method based on prototype learning and introduce a metric to compare a structured set of embeddings.
  arXiv  Detail & Related papers  (2025-08-18T13:51:36Z)
• Fault detection and diagnosis for the engine electrical system of a space launcher based on a temporal convolutional autoencoder and calibrated classifiers [0.0]
  This paper outlines a first step toward developing an onboard fault detection and diagnostic capability for the next generation of reusable space launchers.<n>Unlike existing approaches in the literature, our solution is designed to meet a broader range of key requirements.<n>The proposed solution is based on a temporal convolutional autoencoder to automatically extract low-dimensional features from raw sensor data.
  arXiv  Detail & Related papers  (2025-07-17T11:50:29Z)
• Tracking the risk of a deployed model and detecting harmful distribution shifts [105.27463615756733]
  In practice, it may make sense to ignore benign shifts, under which the performance of a deployed model does not degrade substantially. We argue that a sensible method for firing off a warning has to both (a) detect harmful shifts while ignoring benign ones, and (b) allow continuous monitoring of model performance without increasing the false alarm rate.
  arXiv  Detail & Related papers  (2021-10-12T17:21:41Z)
• Tradeoffs in Streaming Binary Classification under Limited Inspection Resources [14.178224954581069]
  We consider an imbalanced binary classification problem, where events arrive sequentially and only a limited number of suspicious events can be inspected. We analytically characterize the tradeoff between the minority-class detection rate and the inspection capacity. We implement the selection methods on a real public fraud detection dataset and compare the empirical results with analytical bounds.
  arXiv  Detail & Related papers  (2021-10-05T23:23:11Z)
• Weakly-supervised Joint Anomaly Detection and Classification [11.37307883423629]
  Anomaly activities need immediate actions for preventing loss of human life and property in real world surveillance systems. Recent automation in surveillance systems are capable of detecting the anomalies, but they still need human efforts for categorizing the anomalies. We propose a method that jointly handles the anomaly detection and classification in a single framework by adopting a weakly-supervised learning paradigm.
  arXiv  Detail & Related papers  (2021-08-20T04:31:07Z)
• Anomaly Detection in Cybersecurity: Unsupervised, Graph-Based and Supervised Learning Methods in Adversarial Environments [63.942632088208505]
  Inherent to today's operating environment is the practice of adversarial machine learning. In this work, we examine the feasibility of unsupervised learning and graph-based methods for anomaly detection. We incorporate a realistic adversarial training mechanism when training our supervised models to enable strong classification performance in adversarial environments.
  arXiv  Detail & Related papers  (2021-05-14T10:05:10Z)
• No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems [95.54151664013011]
  We present a novel framework to generate adversarial spoofing signals that violate physical properties of the system. We analyze four anomaly detectors published at top security conferences.
  arXiv  Detail & Related papers  (2020-12-07T11:02:44Z)
• Understanding Classifier Mistakes with Generative Models [88.20470690631372]
  Deep neural networks are effective on supervised learning tasks, but have been shown to be brittle. In this paper, we leverage generative models to identify and characterize instances where classifiers fail to generalize. Our approach is agnostic to class labels from the training set which makes it applicable to models trained in a semi-supervised way.
  arXiv  Detail & Related papers  (2020-10-05T22:13:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.