RC-NF：面向机器人操作实时异常检测的机器人条件归一化流
RC-NF: Robot-Conditioned Normalizing Flow for Real-Time Anomaly Detection in Robotic Manipulation
把归一化流的概率密度当作实时 OOD 打分器，挂在 VLA 模型（如 Pi0）后面做即插即用的异常监控与回滚/重规划触发，为视觉研究者提供了一个可复用的『生成式密度估计做机器人异常检测』范式，以及一个含三类机器人异常的仿真基准。
原文依据 RC-NF operates as a plug-and-play module for VLA models (e.g., Pi0) , providing a real-time OOD signal
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期视觉-语言-动作（VLA）模型的进展使机器人能够执行日益复杂的任务。然而，通过模仿学习训练的 VLA 模型难以在动态环境中可靠运行，并常在分布外（OOD）条件下失败。为解决该问题，我们提出机器人条件归一化流（RC-NF），一种用于机器人异常检测与干预的实时监控模型，确保机器人状态与物体运动轨迹同任务保持一致。RC-NF 在归一化流内解耦了对任务感知的机器人状态与物体状态的处理。它仅需正样本进行无监督训练，并在推理时通过概率密度函数计算准确的机器人异常分数。我们进一步提出 LIBERO-Anomaly-10，一个包含三类机器人异常、用于仿真评估的基准。在监控机器人任务方面，与先前方法相比，RC-NF 在所有异常类型上均取得最优性能。真实世界实验表明，RC-NF 可作为 VLA 模型（如 Pi0）的即插即用模块，提供实时 OOD 信号，在必要时支持状态级回滚或任务级重规划，响应延迟低于 100 ms。这些结果表明，RC-NF 显著增强了基于 VLA 的机器人系统在动态环境中的鲁棒性与适应性。
英文原文摘要 1446 字符
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.