Zero-shot object navigation (ZSON) requires robots to locate target objects in unseen environments without task-specific fine-tuning or pre-built maps, a capability crucial for service and household robotics. Existing methods perform well in simulation but struggle in realistic, cluttered environments where heavy occlusions and latent hazards make large portions of the scene unobserved. These approaches typically act on a single inferred scene, making them prone to overcommitment and unsafe behavior under uncertainty. To address these challenges, we propose Schrödinger's Navigator, a belief-aware framework that explicitly reasons over multiple trajectory-conditioned imagined 3D futures at inference time. A trajectory-conditioned 3D world model generates hypothetical observations along candidate paths, maintaining a superposition of plausible scene realizations. An adaptive, occluder-aware trajectory sampling strategy focuses imagination on uncertain regions, while a Future-Aware Value Map (FAVM) aggregates imagined futures to guide robust, proactive action selection. Evaluations in simulation and on a physical Go2 quadruped robot demonstrate that Schrödinger's Navigator outperforms strong ZSON baselines, achieving more robust self-localization, object localization, and safe navigation under severe occlusions and latent hazards. These results highlight the effectiveness of reasoning over imagined 3D futures as a scalable and generalizable strategy for zero-shot navigation in uncertain real-world environments.

翻译：零样本物体导航（ZSON）要求机器人在无任务特定微调或预建地图的情况下，在未见环境中定位目标物体，这一能力对服务机器人和家庭机器人至关重要。现有方法在仿真中表现良好，但在实际杂乱环境中难以适用——严重遮挡与潜在危险导致场景大部分区域未被观测。这类方法通常基于单一推断场景行动，在不确定性下易出现过度承诺及不安全行为。为应对这些挑战，我们提出薛定谔的导航者（Schrödinger's Navigator），一种信念感知框架，在推理时显式推演多种由轨迹条件化的想象三维未来。一个轨迹条件化的三维世界模型沿候选路径生成假设观测，维持多种合理场景实现的叠加态。一种自适应、感知遮挡物的轨迹采样策略将想象力集中于不确定区域，同时未来感知价值图（FAVM）聚合想象未来以引导鲁棒、主动的动作选择。在仿真及物理Go2四足机器人上的评估表明，薛定谔的导航者优于强基线ZSON方法，在严重遮挡与潜在危险下实现了更鲁棒的自身定位、物体定位及安全导航。这些结果凸显了将想象三维未来推理作为在不确定真实环境中实现零样本导航的可扩展且泛化策略的有效性。