Vision-guided robotic systems are increasingly deployed in precision alignment tasks that require reliable execution under near-field and off-axis configurations. While recent advances in pose estimation have significantly improved numerical accuracy, practical robotic systems still suffer from frequent execution failures even when pose estimates appear accurate. This gap suggests that pose accuracy alone is insufficient to guarantee execution-level reliability. In this paper, we reveal that such failures arise from a deterministic geometric error amplification mechanism, in which small pose estimation errors are magnified through system structure and motion execution, leading to unstable or failed alignment. Rather than modifying pose estimation algorithms, we propose a Reliability-aware Execution Gating mechanism that operates at the execution level. The proposed approach evaluates geometric consistency and configuration risk before execution, and selectively rejects or scales high-risk pose updates. We validate the proposed method on a real UR5 robotic platform performing single-step visual alignment tasks under varying camera-target distances and off-axis configurations. Experimental results demonstrate that the proposed execution gating significantly improves task success rates, reduces execution variance, and suppresses tail-risk behavior, while leaving average pose accuracy largely unchanged. Importantly, the proposed mechanism is estimator-agnostic and can be readily integrated with both classical geometry-based and learning-based pose estimation pipelines. These results highlight the importance of execution-level reliability modeling and provide a practical solution for improving robustness in near-field vision-guided robotic systems.

翻译：视觉引导机器人系统在精密对准任务中的部署日益增多，这些任务要求在近场和离轴配置下实现可靠执行。尽管位姿估计的最新进展显著提高了数值精度，但实际的机器人系统即使在其位姿估计看似准确时，仍频繁遭遇执行失败。这一差距表明，仅凭位姿精度不足以保证执行层面的可靠性。本文揭示，此类失败源于一种确定性的几何误差放大机制，即微小的位姿估计误差通过系统结构和运动执行被放大，从而导致不稳定或失败的对准。我们并未修改位姿估计算法，而是提出一种在**执行层面**运作的**可靠性感知执行门控**机制。该方法在执行前评估几何一致性与配置风险，并选择性地拒绝或缩放高风险的位姿更新。我们在一个真实的UR5机器人平台上验证了所提方法，该平台在变化的相机-目标距离和离轴配置下执行单步视觉对准任务。实验结果表明，所提出的执行门控显著提高了任务成功率，降低了执行方差，并抑制了尾部风险行为，同时平均位姿精度基本保持不变。重要的是，该机制与估计器无关，可轻松集成到基于经典几何和基于学习的位姿估计流程中。这些结果凸显了执行层面可靠性建模的重要性，并为提升近场视觉引导机器人系统的鲁棒性提供了一种实用解决方案。