What Would Removing This Corruption Change? Failure Attribution for Vision-Language-Action Models
Vision-language-action (VLA) models are sensitive to visual corruptions, but knowing that a corruption is typically harmful does not establish whether removing it would benefit the current episode. Furthermore, when several corruptions co-occur, deciding what to correct requires understanding which ones contribute to failure risk and how much their removal would help. We define this problem as failure attribution. Detecting corruption presence alone cannot answer this question, as corruptions can be present but relatively harmless, while directly measuring removal benefits requires costly paired policy rollouts with and without the corruption present. To address this, we introduce a facto...
| Authors | Carson Sobolewski, Anjali Parashar, Fei Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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