Navigation among movable obstacles (NAMO) is a critical task in robotics, often challenged by real-world uncertainties such as observation noise, model approximations, action failures, and partial observability. Existing solutions frequently assume ideal conditions, leading to suboptimal or risky decisions. This paper introduces NAMOUnc, a novel framework designed to address these uncertainties by integrating them into the decision-making process. We first estimate them and compare the corresponding time cost intervals for removing and bypassing obstacles, optimizing both the success rate and time efficiency, ensuring safer and more efficient navigation. We validate our method through extensive simulations and real-world experiments, demonstrating significant improvements over existing NAMO frameworks. More details can be found in our website: https://kai-zhang-er.github.io/namo-uncertainty/

翻译：可移动障碍物导航（NAMO）是机器人学中的关键任务，常受到现实世界不确定性的挑战，如观测噪声、模型近似、动作失败和部分可观测性。现有解决方案通常假设理想条件，导致决策次优或存在风险。本文提出NAMOUnc，一种通过将不确定性整合至决策过程来应对这些挑战的新型框架。我们首先估计不确定性，并比较移除与绕行障碍物对应的时间成本区间，在优化成功率与时间效率的同时，确保导航过程更安全高效。通过大量仿真与真实实验验证了所提方法，结果表明其相较现有NAMO框架有显著提升。更多细节请访问我们的网站：https://kai-zhang-er.github.io/namo-uncertainty/