Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2026

Abstract

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 have demonstrated that our RC-NF noticeably enhances the robustness and adaptability of VLA-based robotic systems in dynamic environments.

Discipline

Artificial Intelligence and Robotics | Electrical and Computer Engineering

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026), Denver, Colorado, United States, June 3-7

First Page

43050

Last Page

43060

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Additional URL

https://openaccess.thecvf.com/content/CVPR2026/html/Zhou_RC-NF_Robot-Conditioned_Normalizing_Flow_for_Real-Time_Anomaly_Detection_in_Robotic_CVPR_2026_paper.html

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