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The paper introduces Robot-Conditioned Normalizing Flow (RC-NF), a real-time anomaly detection model for robotic manipulation that monitors the alignment of robot state and object trajectory with the intended task. RC-NF uses normalizing flows to model the probability density of task-aware robot and object states, trained solely on positive examples. Experiments on a new benchmark, LIBERO-Anomaly-10, and in real-world settings demonstrate that RC-NF achieves state-of-the-art anomaly detection performance with sub-100ms latency, enabling effective rollback or replanning for VLA models.
VLA-controlled robots can now detect anomalies in under 100ms using a plug-and-play module, enabling real-time recovery from unexpected situations.
Recent advances in Vision-Language-Action (VLA) models have enabled robots to execute increasingly complex tasks. However, VLA models trained through imitation learning struggle to operate reliably in dynamic environments and often fail under Out-of-Distribution (OOD) conditions. To address this issue, we propose Robot-Conditioned Normalizing Flow (RC-NF), a real-time monitoring model for robotic anomaly detection and intervention that ensures the robot's state and the object's motion trajectory align with the task. RC-NF decouples the processing of task-aware robot and object states within the normalizing flow. It requires only positive samples for unsupervised training and calculates accurate robotic anomaly scores during inference through the probability density function. We further present LIBERO-Anomaly-10, a benchmark comprising three categories of robotic anomalies for simulation evaluation. RC-NF achieves state-of-the-art performance across all anomaly types compared to previous methods in monitoring robotic tasks. Real-world experiments demonstrate that RC-NF operates as a plug-and-play module for VLA models (e.g., pi0), providing a real-time OOD signal that enables state-level rollback or task-level replanning when necessary, with a response latency under 100 ms. These results demonstrate that RC-NF noticeably enhances the robustness and adaptability of VLA-based robotic systems in dynamic environments.
