arXiv cs.AI· Zongyao Lyu, Lingling Chen, William J. Beksi·· 14 小时前AI 评分35
SentryVLA：面向视觉-语言-动作机器人控制的不确定性引导动作选择与状态监测
SentryVLA: Uncertainty-Guided Action Selection and State Monitoring for Vision-Language-Action Robotic Control
AI 导读
SentryVLA 是一个面向冻结 VLA 策略的轻量级运行时封装，无需重训练或修改架构即可提升动作选择可靠性。它在动作层用保形分位数回归估计采样动作与专家行为的偏差上界并据此对多个随机候选排序，在执行层用基于 Mahalanobis 距离的异常分数监测闭环控制中的异常状态。仿真与实体机器人实验显示其改善了动作选择并提供有效的运行时预警信号，成果将发表于 CoRL 2026。
正文 · 原文
Abstract: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: this https URL.
| Comments: | To be published in the 2026 Conference on Robot Learning (CoRL) |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2604.16677 [cs.RO] |
| (or arXiv:2604.16677v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2604.16677 arXiv-issued DOI via DataCite |
Submission history
From: William Beksi [view email]
[v1]
Fri, 17 Apr 2026 20:20:43 UTC (15,396 KB)
[v2]
Sun, 25 Oct 2026 02:55:19 UTC (22,852 KB)
来源：arXiv cs.AI · arxiv.org