Vision-Language-Action (VLA) models leverage pretrained Vision-Language Models (VLMs) as backbones to map images and instructions to actions, demonstrating remarkable potential for generalizable robotic manipulation. To enhance performance, existing methods often incorporate extra observation cues (e.g., depth maps, point clouds) or auxiliary modules (e.g., object detectors, encoders) to enable more precise and reliable task execution, yet these typically require costly data collection and additional training. Inspired by the finding that Feed-Forward Network (FFN) in language models can act as "key-value memory", we propose Uncertainty-aware Observation Reinjection (UAOR), an effective, training-free and plug-and-play module for VLA models. Specifically, when the current language model layer exhibits high uncertainty, measured by Action Entropy, it reinjects key observation information into the next layer's Feed-Forward Network (FFN) through attention retrieval. This mechanism directly augments the hidden states with observation evidence at high-uncertainty layers, enabling more accurate and reliable action generation. Comprehensive experiments show that our method consistently improves diverse VLA models across simulation and real-world tasks with minimal overhead. Notably, UAOR eliminates the need for additional observation cues or modules, making it a versatile and practical plug-in for existing VLA pipelines. The project page is at https://uaor.jiabingyang.cn.

翻译：视语言动作（VLA）模型利用预训练的视语言模型（VLM）作为骨干网络，将图像和指令映射到动作，展现出泛化机器人操控的巨大潜力。为提升性能，现有方法常引入额外观测线索（如深度图、点云）或辅助模块（如目标检测器、编码器）以实现更精确可靠的任务执行，但这些方法通常需要昂贵的数据收集和额外训练。受语言模型中前馈网络（FFN）可作为"键值记忆"的启发，我们提出不确定性感知观测重注入（UAOR），一种面向VLA模型的有效、免训练且即插即用的模块。具体而言，当当前语言模型层表现出由动作熵（Action Entropy）衡量的高不确定性时，该方法通过注意力检索将关键观测信息重注入下一层的前馈网络（FFN）。这一机制在高不确定性层直接以观测证据增强隐藏状态，从而生成更准确可靠的动作。全面实验表明，我们的方法以最小开销一致地提升了多种VLA模型在仿真和真实世界任务中的性能。值得注意的是，UAOR无需额外观测线索或模块，成为现有VLA流程中通用且实用的插件。项目页面位于https://uaor.jiabingyang.cn。