FailureSpot: Label-Efficient Timestamp-Level Failure Detection for Vision-Language-Action Models
Vision-language-action (VLA) policies have shown strong potential for general-purpose robotic manipulation, but they can still fail unpredictably during long-horizon execution, making reliable failure detection essential for safe deployment. Existing methods either rely on visual models that typically detect failures only after erroneous actions have occurred, or use lightweight proactive detectors trained on VLA internal representations. However, these proactive methods are often supervised with trajectory-level labels, causing normal pre-failure behavior in unsuccessful trajectories to be incorrectly labeled as failure. This supervision mismatch introduces label noise and limits both tr...
| Authors | Jie Ma, Zongxi Liu, Yi Zhu |
|---|---|
| Primary category | cs.RO |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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