用世界模型+异常检测，自动识别工业巡检中的成功、失败和异常
World Model Failure Classification and Anomaly Detection for Autonomous Inspection
- 基于压缩视频输入的世界模型，结合置信区间判断
- 90%以上准确率区分三类情况，比人工更早发现异常
- 适用于无人巡检系统，可提升训练数据质量
自主巡检机器人可降低工业场所人工巡检的成本与风险，但受遮挡、视角受限或环境突变影响，准确读数仍具挑战。本文提出一种混合框架，融合有监督故障分类与异常检测，将巡检任务分为成功、已知故障或异常（即分布外）三类。该方法采用世界模型作为主干网络，输入压缩视频，通过置信区间阈值设定两个决策函数，在人类观察前完成分类。在办公区与工业现场采集的仪表巡检视频上进行评估，并实现在波士顿动力Spot机器人的实时部署。实验表明，对成功、故障和分布外案例的区分准确率超过90%，且分类时间早于人工观察。结果表明该框架具备鲁棒性与前瞻性故障检测潜力，可用于巡检任务或作为模型训练的数据质量反馈信号。
原文摘要 · Abstract (English)
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
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