Embodied navigation for long-horizon tasks, guided by complex natural language instructions, remains a formidable challenge in artificial intelligence. Existing agents often struggle with robust long-term planning about unseen environments, leading to high failure rates. To address these limitations, we introduce NavForesee, a novel Vision-Language Model (VLM) that unifies high-level language planning and predictive world model imagination within a single, unified framework. Our approach empowers a single VLM to concurrently perform planning and predictive foresight. Conditioned on the full instruction and historical observations, the model is trained to understand the navigation instructions by decomposing the task, tracking its progress, and formulating the subsequent sub-goal. Simultaneously, it functions as a generative world model, providing crucial foresight by predicting short-term environmental dynamics and long-term navigation milestones. The VLM's structured plan guides its targeted prediction, while the imagined future provides rich context to inform the navigation actions, creating a powerful internal feedback loop of perception-planning/prediction-action. We demonstrate through extensive experiments on the R2R-CE and RxR-CE benchmark that NavForesee achieves highly competitive performance in complex scenarios. Our work highlights the immense potential of fusing explicit language planning with implicit spatiotemporal prediction, paving the way for more intelligent and capable embodied agents.

翻译：在人工智能领域，由复杂自然语言指令引导的具身导航执行长时程任务仍是一项艰巨挑战。现有智能体通常难以对未见环境进行鲁棒的长时程规划，导致失败率较高。为应对这些局限，我们提出NavForesee——一种新颖的视觉-语言模型，它将高层语言规划与预测性世界模型想象统一于单一框架中。我们的方法使单个视觉-语言模型能够同时执行规划与预测性前瞻。在完整指令与历史观测的条件下，该模型通过分解任务、追踪进度并制定后续子目标来学习理解导航指令。同时，它作为生成式世界模型，通过预测短期环境动态与长期导航里程碑提供关键前瞻信息。该视觉-语言模型的结构化规划指导其定向预测，而想象出的未来则为导航动作提供丰富上下文，从而形成感知-规划/预测-行动的强大内部反馈循环。我们在R2R-CE与RxR-CE基准测试中通过大量实验证明，NavForesee在复杂场景中实现了极具竞争力的性能。本研究凸显了显式语言规划与隐式时空预测相融合的巨大潜力，为开发更智能、更强大的具身智能体开辟了新途径。