VLA models have shown promising potential in embodied navigation by unifying perception and planning while inheriting the strong generalization abilities of large VLMs. However, most existing VLA models rely on reactive mappings directly from observations to actions, lacking the explicit reasoning capabilities and persistent memory required for complex, long-horizon navigation tasks. To address these challenges, we propose VLingNav, a VLA model for embodied navigation grounded in linguistic-driven cognition. First, inspired by the dual-process theory of human cognition, we introduce an adaptive chain-of-thought mechanism, which dynamically triggers explicit reasoning only when necessary, enabling the agent to fluidly switch between fast, intuitive execution and slow, deliberate planning. Second, to handle long-horizon spatial dependencies, we develop a visual-assisted linguistic memory module that constructs a persistent, cross-modal semantic memory, enabling the agent to recall past observations to prevent repetitive exploration and infer movement trends for dynamic environments. For the training recipe, we construct Nav-AdaCoT-2.9M, the largest embodied navigation dataset with reasoning annotations to date, enriched with adaptive CoT annotations that induce a reasoning paradigm capable of adjusting both when to think and what to think about. Moreover, we incorporate an online expert-guided reinforcement learning stage, enabling the model to surpass pure imitation learning and to acquire more robust, self-explored navigation behaviors. Extensive experiments demonstrate that VLingNav achieves state-of-the-art performance across a wide range of embodied navigation benchmarks. Notably, VLingNav transfers to real-world robotic platforms in a zero-shot manner, executing various navigation tasks and demonstrating strong cross-domain and cross-task generalization.

翻译：视觉语言动作（VLA）模型通过统一感知与规划，并继承大型视觉语言模型（VLM）的强大泛化能力，在具身导航领域展现出巨大潜力。然而，现有大多数VLA模型依赖于从观测到动作的直接反应式映射，缺乏复杂、长程导航任务所需的显式推理能力和持久记忆。为应对这些挑战，我们提出VLingNav——一个基于语言驱动认知的具身导航VLA模型。首先，受人类认知双过程理论启发，我们引入一种自适应思维链机制，该机制仅在必要时动态触发显式推理，使智能体能够在快速直觉执行与缓慢审慎规划之间流畅切换。其次，为处理长程空间依赖关系，我们开发了一个视觉辅助语言记忆模块，该模块构建了一个持久的跨模态语义记忆，使智能体能够回忆过往观测以避免重复探索，并推断动态环境中的运动趋势。在训练方案上，我们构建了Nav-AdaCoT-2.9M——迄今为止规模最大的带推理标注的具身导航数据集，其中包含可引导模型形成“何时思考”与“思考什么”自适应决策能力的新型自适应思维链标注。此外，我们引入在线专家引导的强化学习阶段，使模型能够超越纯模仿学习，获得更鲁棒、更具自主探索性的导航行为。大量实验表明，VLingNav在广泛的具身导航基准测试中均达到了最先进的性能。值得注意的是，VLingNav能够以零样本方式迁移至真实世界机器人平台，执行多种导航任务，并展现出强大的跨领域与跨任务泛化能力。