ActFovea: Runtime Safeguarding for VLA Policies via Spatiotemporal Visual-Action Consistency
Vision-language-action (VLA) policies achieve strong performance in robotic manipulation but remain vulnerable to runtime disturbances that break the temporal alignment among visual observations, robot states, and executed actions. We introduce ActFovea, a plug-and-play safeguarding framework that detects and mitigates such failures without retraining or modifying the underlying VLA policy. ActFovea uses robot kinematics, proprioceptive states, and recent actions to construct action-conditioned foveated regions that retain contact-relevant areas and predicted motion corridors while suppressing task-irrelevant visual content. It detects runtime risks by evaluating whether visual motion and...
| Authors | Wenda Yu, Tianshi Wang, Fengling Li et al. |
|---|---|
| Primary category | cs.RO |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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