Zero-shot object navigation (ZSON) requires robots to find target objects in unseen environments without task-specific fine-tuning or pre-built maps, a key capability for general-purpose service robots. Yet methods that perform well in simulation often degrade in cluttered real-world scenes with severe occlusion and latent hazards, where large unseen regions make single-scene inference brittle and unsafe. We propose Schrödinger's Navigator, a belief-aware framework that reasons at inference time over multiple trajectory-conditioned imagined 3D futures. Given candidate paths, a trajectory-conditioned 3D world model predicts hypothetical observations and maintains a superposition of plausible scene realizations rather than committing to one map. An adaptive occluder-aware sampler directs imagination to uncertainty-critical regions, while a Future-Aware Value Map (FAVM) aggregates imagined futures for robust, proactive action selection. Experiments in simulation and on a physical Go2 quadruped show that Schrödinger's Navigator outperforms strong ZSON baselines, improving hidden-target discovery and risk-aware waypoint selection in occlusion-heavy navigation scenarios. These results highlight imagined 3D futures as a scalable and generalizable strategy for zero-shot navigation in uncertain real-world environments.

翻译：零样本目标导航（ZSON）要求机器人在未见过环境中寻找目标物体，无需任务特定微调或预建地图，这是通用服务机器人的关键能力。然而，在仿真中表现良好的方法在严重遮挡和潜在障碍的杂乱真实场景中往往性能下降，其中大范围未观测区域使得单一场景推理变得脆弱且不安全。我们提出《薛定谔的导航者》，一种基于信念感知的框架，在推理阶段对多个轨迹条件化的三维未来想象进行推演。针对候选路径，轨迹条件化的三维世界模型预测假设观测并维持多种可行场景的叠加状态，而非固守单一地图。自适应遮挡物感知采样器将想象引导至不确定性关键区域，而未来感知价值图（FAVM）聚合所设想的未来信息以实现鲁棒且前瞻性的动作选择。仿真实验与实体Go2四足机器人上的测试表明，《薛定谔的导航者》超越强性能ZSON基准，在重度遮挡导航场景中提升了隐藏目标发现能力与风险感知路径点选择效果。这些结果凸显了想象三维未来作为不确定性真实环境中零样本导航的可扩展与可泛化策略的潜力。