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This research introduces a novel factorized risk model for vision-language-action (VLA) systems to quantify the specific impact of visual corruptions on task failure. By estimating removal benefits through latent intervention rather than costly paired rollouts, the method enables efficient online failure monitoring and correction in robotic hardware deployments.
What Would Removing This Corruption Change? Failure Attribution for Vision-Language-Action Models
arXiv:2610.27277v1 Announce Type: cross Abstract: 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 factorized risk model that learns separate representations of the scene and corruptions, allowing us to estimate removal benefits through latent intervention from the current image and task instruction, without paired clean images or additional counterfactual rollouts. We evaluate our approach on OpenVLA and $\pi_{0.5}$ in simulation and $\pi_{0.5}$-DROID on a Franka robot. Our method outperforms monolithic and vision-language-model (VLM) baselines, including achieving approximately 3x lower attribution error than frontier VLMs. We also demonstrate our method adapts to execution context and corruption intensity, and can be used for online failure monitoring on hardware.
Title:What Would Removing This Corruption Change? Failure Attribution for Vision-Language-Action Models
Abstract: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 factorized risk model that learns separate representations of the scene and corruptions, allowing us to estimate removal benefits through latent intervention from the current image and task instruction, without paired clean images or additional counterfactual rollouts. We evaluate our approach on OpenVLA and $\pi_{0.5}$ in simulation and $\pi_{0.5}$-DROID on a Franka robot. Our method outperforms monolithic and vision-language-model (VLM) baselines, including achieving approximately 3x lower attribution error than frontier VLMs. We also demonstrate our method adapts to execution context and corruption intensity, and can be used for online failure monitoring on hardware.
Robotics (cs.RO); Machine Learning (cs.LG)
arXiv:2610.27277 [cs.RO]
(or arXiv:2610.27277v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.27277 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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