Reliable trajectory planning under partial observability depends not only on computing a feasible geometric path, but also on whether the robot receives informative observations while executing that trajectory. Existing approaches usually keep the environment fixed and adapt the robot through belief-space planning, active localization, or added sensing, often incurring costly uncertainty propagation and brittle behavior in observation-poor regions. We flip this perspective and address the largely open problem of \emph{task-aware environment augmentation}: given a mapped environment, a planned task trajectory, and a small budget of visual fiducial markers, where should the environment be augmented so that the planned trajectory can be executed reliably under uncertainty? Our key observation is that useful marker layouts are defined by the localization support they provide along the task trajectory: a small number of well-timed observations can be sufficient to prevent uncertainty from accumulating in regions where state-estimation error would otherwise compromise control. Building on this observation, we present \tbp{SCoDA}, $\textbf{S}$hielded $\textbf{Co}$nditional $\textbf{D}$iffusion for Environment $\textbf{A}$ugmentation. \tbp{SCoDA} learns a conditional distribution over high-performing fiducial layouts from data, using the environment, planned trajectory, disturbance context, and desired execution profile as conditioning. Its shielded sampler reasons over where along the planned execution pose corrections should occur, and steers this distribution toward task-relevant, finite-budget augmentations. Across simulated benchmarks and hardware deployments, we show that \tbp{SCoDA} improves trajectory execution reliability and completion time over strong baselines. Code, models and dataset available at: \hyperlink{scoda-diffusion.github.io}{https://scoda-diffusion.github.io/}

翻译：在部分可观测条件下实现可靠轨迹规划，不仅依赖于计算可行的几何路径，还取决于机器人执行该轨迹过程中能否获取具有信息量的观测数据。现有方法通常保持环境固定不变，通过信念空间规划、主动定位或附加传感来适配机器人，这往往导致观测信息匮乏区域出现高昂的不确定性传播代价和脆弱的行为表现。我们转换这一视角，解决一个尚待深入研究的开放性问题——**任务感知的环境增强**：在给定已建图环境、规划的任务轨迹和少量视觉标签标记预算的条件下，应如何在环境中增强标记布局，使规划轨迹能在不确定性条件下可靠执行？我们的关键发现是：有用标记布局的特性取决于其沿任务轨迹提供的定位支持能力——少量恰当时间点的观测足以防止状态估计误差在可能破坏控制的区域累积不确定性。基于此发现，我们提出**SCoDA**（屏蔽式条件扩散用于环境增强）。SCoDA从数据中学习高性能标签布局的条件分布，以环境、规划轨迹、扰动上下文和期望执行剖面作为条件信息。其屏蔽式采样器推理沿规划执行应发生位姿修正的位置，并将该分布导向任务相关且有限预算的增强方案。在仿真基准测试和硬件实物部署中，我们证明SCoDA较之强基线方法显著提升了轨迹执行可靠性和任务完成时间。代码、模型与数据集详见：\hyperlink{scoda-diffusion.github.io}{https://scoda-diffusion.github.io/}