SAFECAST: Robust Failure Detection for VLA Policies with Contrast-Set Training and Calibration

Abstract:Vision-language-action policies often fail under deployment-time distribution shifts such as clutter, distractor objects, lighting changes, novel objects, altered initial states, and reworded instructions. Hidden-state-based risk probes combined with functional conformal prediction can detect rollout failures, but their reliability depends on calibration data matching deployment conditions. We introduce SAFECAST, which leverages contrast set perturbations to improve hidden-state probe training and calibration for deployment time shift. SAFECAST statistically significantly improves failure detection ROC-AUC scores over a state of the art baseline in both real-world DROID and LIBERO simulation experiments across multiple VLM backbones. We further find that SAFECAST benefits most when both visual and language contrast set perturbations are used to augment data, and that with contrast set perturbations, sim-to-real calibration leads to better probes than using real rollout data only.

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2608.04246 [cs.RO]

(or arXiv:2608.04246v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.04246 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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