CalVLA: Shaping Native Confidence in Vision-Language-Action Policies with Outcome Supervision

Confidence estimation for Vision-Language-Action (VLA) models is essential for robots to perform manipulation tasks in the open world, providing crucial signals for risk-sensitive decision-making and failure anticipation. Existing confidence estimation methods typically rely on ensemble-based paradigms or action-token probabilities to predict the likelihood of task success. However, they still encounter challenges in computational efficiency and cross-architecture generalizability. These methods usually require repeated sampling, leading to inference inefficiency, and are restricted to VLA models with discrete action outputs, making them difficult to apply to continuous action spaces. To address this issue, we propose VLAConf, a one-class discriminative confidence framework. By leveraging frozen pretrained VLA internal representations, VLAConf directly estimates step-wise anomaly scores in a single forward pass using a lightweight confidence head, thereby eliminating the overhead of exhaustive resampling. We additionally use step-conditioned modeling to encode rollout-phase information along the manipulation trajectory. Experiments on the LIBERO benchmark demonstrate that VLAConf significantly improves the quality of the confidence signal constructed for post-hoc calibration, outperforming existing baselines by a large margin in inference efficiency. The effectiveness of VLAConf is further validated in real-robot experiments. To access the source code and supplementary videos, visit https://sites.google.com/view/vlaconf.

Vision Language Action models combine perception, language grounding, and control in a single policy, but their failures are hard to diagnose once visual conditions shift. We test whether OpenVLA feedforward activations contain linearly decodable information about near term task failure in LIBERO manipulation rollouts. The policy is fixed throughout. We log internal activations during execution and fit lightweight monitors after the rollouts are collected. Occlusion is the main controlled stress test. It reduces OpenVLA success from $57\%$ to $17\%$ over $100$ episodes per condition. Under this shift, a logistic probe at layer 16 reaches AUROC $0.972$ and AUPRC $0.352$ for predicting failure within a $15$ step horizon. It outperforms both a mean difference direction and an action disagreement baseline. A sparse layer sweep finds uneven decodability across depth: layer 16 is strongest among the tested layers, layer 8 remains informative, and layer 10 is weaker. To check whether the monitor is just an occlusion detector, we also evaluate color shift and camera jitter without refitting. Color shift produces no failures in this setting, so it is a benign control rather than a failure benchmark. Camera jitter does induce failures, and the occlusion trained monitor remains above random. The result is deliberately limited: OpenVLA internal states contain failure relevant structure under controlled perceptual shift, but these experiments do not establish a causal mechanism, task held out generalization, or a deployable recovery system.

Vision-language-action (VLA) models have emerged as generalist robotic controllers that map visual observations and language instructions to continuous actions. However, pretrained VLAs typically provide limited information about the reliability of their generated actions, making deployment challenging when execution failures must be anticipated. We address this problem by introducing SentryVLA, a lightweight runtime wrapper for frozen VLA policies. At the action level, SentryVLA uses conformalized quantile regression to estimate a calibrated upper bound on the deviation between sampled VLA actions and expert-like behavior under the calibration distribution. The predicted deviation is then used to rank multiple stochastic candidates for action selection. At the execution-monitoring level, SentryVLA applies a Mahalanobis-distance-based anomaly score to the contextualized latent execution state extracted before final action prediction, providing a complementary signal for detecting abnormal execution states during closed-loop control. The framework does not require retraining or architectural modification of the underlying VLA policy. Experiments in simulation and on a physical robot show that SentryVLA improves action selection and provides useful runtime warning signals for abnormal execution states. These findings demonstrate the practical value of post-hoc reliability scoring for improving the runtime behavior of pretrained VLA policies. Project webpage: https://robotic-vision-lab.github.io/sentryvla/.