从机器人部署日志中自动发现可解释的故障分类体系。
Unsupervised Discovery of Failure Taxonomies from Deployment Logs
- 用多模态视觉语言模型推断故障原因，再在语义空间聚类。
- 在三个领域验证，发现的故障分类具一致性和实用性。
- 适合做系统优化和实时故障监控的团队使用。
随着机器人系统日益融入真实环境（如自动驾驶与家用服务），它们会遭遇多样且无结构的场景导致故障。这些故障虽带来安全与可靠性挑战，却也蕴含丰富感知数据以提升系统鲁棒性。然而，人工分析大规模故障数据集不切实际且难以扩展。本文提出从大量原始故障日志中无监督发现故障分类体系的问题，目标是从感知轨迹中直接获得语义连贯且可操作的故障模式。方法首先利用视觉-语言推理从多模态输入中推断结构化故障解释，然后在生成的语义推理空间中进行聚类，发现重复出现的故障模式而非孤立的单次事件描述。我们在机器人操作、室内导航与自动驾驶三个领域评估该方法，结果表明所发现的分类体系具有高一致性、可解释性，并具备实际应用价值。尤其证明结构化故障分类可指导离线策略优化的数据采集，增强运行时故障监测系统的效能。
原文摘要 · Abstract (English)
As robotic systems become increasingly integrated into real-world environments, ranging from autonomous vehicles to household assistants, they inevitably encounter diverse and unstructured scenarios that lead to failures. While such failures pose safety and reliability challenges, they also provide rich perceptual data for improving system robustness. However, manually analyzing large-scale failure datasets is impractical and does not scale. In this work, we introduce the problem of unsupervised discovery of failure taxonomies from large volumes of raw failure logs, aiming to obtain semantically coherent and actionable failure modes directly from perceptual trajectories. Our approach first infers structured failure explanations from multimodal inputs using vision language reasoning, then clusters them in the resulting semantic reasoning space, discovering recurring failure modes rather than isolated episode-level descriptions. We evaluate our method across robotic manipulation, indoor navigation, and autonomous driving domains, demonstrating that the discovered taxonomies are consistent, interpretable, and useful in practice. In particular, we show that structured failure taxonomies guide targeted data collection for offline policy refinement and enhance runtime failure monitoring systems. Website: https://mllm-failure-clustering.github.io/
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