Task-Aware Environment Augmentation for Reliable Navigation via Shielded Conditional Diffusion

2026-06-13

AI Summary

In partially observable environments, robots often suffer from state estimation drift and control failure due to insufficient observations. This work proposes a task-aware environmental augmentation approach that formulates visual fiducial marker placement as a conditional generation problem for the first time. The authors introduce the Shielded Conditional Diffusion Architecture (SCoDA), which generates high-utility marker configurations under budget constraints, conditioned on environment maps, task trajectories, perturbation scenarios, and desired execution profiles. By strategically deploying a minimal number of markers at critical locations, the method effectively suppresses the accumulation of state estimation errors. Experimental results demonstrate that the proposed approach significantly outperforms existing baselines in both simulation and real-world platforms, substantially improving trajectory execution success rates and reducing task completion time.

Abstract

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/}

Problem

• task-aware environment augmentation
• partial observability
• trajectory planning
• fiducial markers
• reliable navigation

Innovation

• task-aware environment augmentation
• conditional diffusion
• shielded sampling
• fiducial marker placement
• reliable navigation