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SentryVLA: Uncertainty-Guided Action Selection and State Monitoring for Vision-Language-Action Robotic Control

SentryVLA is a lightweight runtime wrapper for frozen VLA policies that requires no retraining or changes to the model architecture. At the action level, it uses conformal quantile regression to estimate an upper bound on the deviation between sampled actions and expert behavior, and ranks multiple stochastic candidate actions accordingly; at the execution level, it uses a Mahalanobis-distance anomaly score to monitor abnormal states during closed-loop control. Simulation and real-world experiments show that the method improves action selection and provides useful runtime warning signals.

Part of the event: SentryVLA: A Runtime Reliability Wrapper for VLA Without Retraining

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