軌跡中的捉迷藏：探索失敗訊號以進行VLA運行時監控

摘要

視覺-語言-行動（VLA）模型使機器人能遵循自然語言指令並在多元任務中展現泛化能力，然而這類模型在實際部署時仍容易因執行失敗而損害系統可靠性。因此，在執行過程中檢測此類失誤，對於穩健部署具身系統至關重要。現有的故障檢測方法，或依賴於昂貴的動作重新取樣與外部模型，或將軌跡層級的標籤均勻分布至每個時間步，從而掩蓋了局部故障信號。本文提出「捉迷藏」（Hide-and-Seek）框架，將VLA故障檢測形式化為粗監督學習問題。透過結合軌跡間與軌跡內對比目標，Hide-and-Seek能在僅依靠軌跡層級監督（無需任何時間步層級標註）的情況下，定位出表徵故障的動作，並誘導出具有時間結構的故障信號。我們在LIBERO、VLABench以及實際機器人平台上，針對三種具代表性的VLA策略——OpenVLA、π_0與π_{0.5}——評估Hide-and-Seek。實驗結果顯示，該方法在共形預測下達成了準確率與時效性的實用權衡，並在已見與未見任務上均展現良好的泛化能力，取得最先進的多任務故障檢測表現。

English

Vision-Language-Action (VLA) models enable robots to follow natural language instructions and generalize across diverse tasks, but they remain vulnerable to execution failures that compromise reliability in real-world deployment. Detecting such failures during execution is therefore critical for the robust deployment of embodied systems. Existing failure detection methods either rely on expensive action resampling or external models, while alternatives propagate trajectory-level labels uniformly across every timestep, obscuring localized failure signals. In this paper, we propose Hide-and-Seek, a framework that formulates VLA failure detection as a coarsely supervised learning problem. By combining inter-trajectory and intra-trajectory contrastive objectives, Hide-and-Seek localizes failure-indicative actions and induces temporally structured failure signals from trajectory-level supervision alone, without any step-level annotation. We evaluate Hide-and-Seek on LIBERO, VLABench, and a real-world robotic platform across three representative VLA policies: OpenVLA, π_0, and π_{0.5}.Our method achieves state-of-the-art multi-task failure detection performance with a practical accuracy--timeliness trade-off under conformal prediction, and generalizes well to both seen and unseen tasks.