Abstract
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.
Abstract (translated)
VLA模型在具身导航中展现出巨大的潜力，通过统一感知与规划，并继承大型视觉语言模型（VLMs）的强大泛化能力。然而，大多数现有的VLA模型依赖于从观察到动作的反应式映射，缺乏执行复杂、长期任务所需的明确推理能力和持久记忆功能。为了解决这些挑战，我们提出了VLingNav，这是一种基于语言驱动认知的具身导航VLA模型。 首先，借鉴人类认知的双过程理论，我们引入了一种适应性的链式思维机制（chain-of-thought），该机制能根据需要动态触发明确推理，使代理能够在快速直观执行和慢速深思熟虑规划之间灵活切换。其次，为了处理长期的空间依赖关系，我们开发了一个辅助语言记忆模块，构建持久的跨模态语义记忆，使代理能够回忆过去的观察结果以避免重复探索，并推断出动态环境中的移动趋势。 在训练策略方面，我们构建了Nav-AdaCoT-2.9M，这是迄今为止最大的具有推理注释的具身导航数据集，包含适应性链式思维（CoT）注释，能够诱导一种既考虑何时思考也考虑思考什么内容的推理模式。此外，我们还融入了一个在线专家指导增强学习阶段，使模型超越纯粹模仿学习，并获得更为稳健、自我探索的导航行为。 广泛的实验表明，VLingNav在各种具身导航基准测试中实现了最先进的性能。值得注意的是，VLingNav能够零样本迁移到真实世界的机器人平台，在执行多种导航任务时表现出强大的跨域和跨任务泛化能力。
URL
https://arxiv.org/abs/2601.08665
