自律点検のための世界モデルによる失敗分類と異常検知
World Model Failure Classification and Anomaly Detection for Autonomous Inspection
世界モデルとConformal Predictionを組み合わせ、点検タスクの成功・既知の失敗・異常（分布外）を人間より早くリアルタイムに分類する枠組みを提案し、Spot上で90%以上の精度を実証した。
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著者: Michelle Ho, Muhammad Fadhil Ginting, Isaac R. Ward, Andrzej Reinke, Mykel J. Kochenderfer, Ali-akbar Agha-Mohammadi, Shayegan Omidshafiei
分類: cs.RO
原文アブストラクト
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
