NavForesee: 一种统一的视觉-语言世界模型用于分层规划和双时间尺度导航预测
NavForesee: A Unified Vision-Language World Model for Hierarchical Planning and Dual-Horizon Navigation Prediction
AI总结：
NavForesee通过统一的视觉-语言模型实现分层规划和双时间尺度导航预测，结合任务分解与环境预测，提升复杂场景下的导航能力。
摘要：
针对长时间任务的具身导航，受复杂自然语言指令引导仍面临巨大挑战。现有代理在未知环境中长期规划时表现不佳，导致高失败率。为此，我们提出NavForesee，一种新的视觉-语言模型，统一高阶语言规划与预测世界模型想象。该模型在单一框架内同时执行规划与预测。基于完整指令和历史观测，模型通过分解任务、跟踪进展并制定子目标来理解导航指令。同时，它作为生成世界模型，通过预测短期环境动态和长期导航里程碑提供关键预测。VLM的结构化计划指导其目标预测，而想象的未来提供丰富上下文以指导导航动作，形成强大的感知-规划/预测-动作反馈循环。我们在R2R-CE和RxR-CE基准上进行了大量实验，证明NavForesee在复杂场景中具有高度竞争性。本工作展示了将显式语言规划与隐式时空预测融合的巨大潜力，为更智能的具身代理铺平道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
