DART: Proactive Runtime Failure Detection for Vision-Language-Action Models

Recent advances in Vision-Language-Action (VLA) models have enabled strong generalization across diverse robotic manipulation tasks, yet their real-world deployment remains limited by the lack of reliable runtime safety mechanisms. Existing failure detection methods typically rely on coarse trajectory-level supervision or reactive failure scoring, limiting their ability to identify subtle early-stage deviations and rapidly emerging failures. We propose DART, a proactive runtime failure detection framework for VLA policies that jointly addresses sudden failures and cumulative long-horizon deviations through a dual-horizon architecture: a short-horizon module leverages internal semantic and action representations from the VLA to capture abrupt behavioral inconsistencies, while a long-horizon module utilizes a video generation world model to anticipate future state drift before failures fully manifest. To provide fine-grained supervision, we further introduce an automated VLM-based annotation pipeline that generates timestep-level failure labels from execution trajectories while reducing the need for dense manual annotation. Extensive experiments across simulated and real-world manipulation environments on multiple VLA policies demonstrate that DART achieves a favorable trade-off between detection accuracy and temporal responsiveness, particularly under unseen tasks.