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

翻译：用于监测工业场所的自主巡检机器人能够降低人工巡检相关的成本与风险。然而，由于遮挡、视角受限或意外环境条件等因素，获取精确读数可能面临挑战。我们提出一种结合监督式故障分类与异常检测的混合框架，能够将巡检任务分类为成功、已知故障或异常（即分布外）情况。该方法采用以压缩视频输入为基础的世界模型主干网络。这一策略无关且无分布假设的框架，通过基于保形预测阈值设定的两个决策函数，在人类观察者之前完成分类判定。我们在从办公及工业场所采集的仪表巡检视频流上评估该框架，并在Boston Dynamics Spot机器人上实现了实时部署。实验表明，该方法在区分成功、故障与分布外情况时准确率超过90%，且分类判断早于人类观察者。这些结果凸显了该框架在自主巡检任务中实现鲁棒、前瞻性故障检测的潜力，或可作为模型训练的反馈信号，用于评估并提升训练数据质量。项目网站：https://autoinspection-classification.github.io