Zero-shot object navigation (ZSON) requires a robot to locate a target object in a previously unseen environment without relying on pre-built maps or task-specific training. However, existing ZSON methods often struggle in realistic and cluttered environments, particularly when the scene contains heavy occlusions, unknown risks, or dynamically moving target objects. To address these challenges, we propose \textbf{Schrödinger's Navigator}, a navigation framework inspired by Schrödinger's thought experiment on uncertainty. The framework treats unobserved space as a set of plausible future worlds and reasons over them before acting. Conditioned on egocentric visual inputs and three candidate trajectories, a trajectory-conditioned 3D world model imagines future observations along each path. This enables the agent to see beyond occlusions and anticipate risks in unseen regions without requiring extra detours or dense global mapping. The imagined 3D observations are fused into the navigation map and used to update a value map. These updates guide the policy toward trajectories that avoid occlusions, reduce exposure to uncertain space, and better track moving targets. Experiments on a Go2 quadruped robot across three challenging scenarios, including severe static occlusions, unknown risks, and dynamically moving targets, show that Schrödinger's Navigator consistently outperforms strong ZSON baselines in self-localization, object localization, and overall Success Rate in occlusion-heavy environments. These results demonstrate the effectiveness of trajectory-conditioned 3D imagination in enabling robust zero-shot object navigation.

翻译：零样本目标导航要求机器人在未经预先建图或任务特定训练的情况下，在陌生环境中定位目标物体。然而，现有零样本目标导航方法在真实复杂环境中常面临挑战，尤其在场景存在严重遮挡、未知风险或动态移动目标时。为解决这些问题，我们提出**薛定谔导航器**——一个受薛定谔不确定性思想实验启发的导航框架。该框架将未观测空间视为一组可能的未来世界，并在行动前对其进行推理。基于以自我为中心的视觉输入和三条候选轨迹，轨迹条件化的三维世界模型可沿每条路径推演未来观测。这使得智能体能够透视遮挡区域，预判未知区域的风险，而无需额外绕行或密集全局建图。推演得到的三维观测被融合至导航地图，并用于更新价值地图。这些更新引导策略选择能够避开遮挡、减少不确定空间暴露并更好追踪移动目标的轨迹。在Go2四足机器人上进行的三种挑战性场景实验（包括严重静态遮挡、未知风险和动态移动目标）表明，薛定谔导航器在遮挡密集环境中的自定位、目标定位及整体成功率方面均持续优于现有零样本目标导航基线方法。这些结果验证了轨迹条件化三维推演在实现鲁棒零样本目标导航方面的有效性。