VLingNav: Embodied Navigation with Adaptive Reasoning and Visual-Assisted Linguistic Memory

TL;DR

VLingNav introduces an adaptive reasoning and persistent memory framework for embodied navigation, significantly improving performance and generalization in complex, long-horizon tasks and enabling zero-shot transfer to real robots.

Contribution

The paper presents VLingNav, a novel embodied navigation model with an adaptive chain-of-thought reasoning mechanism and a visual-assisted linguistic memory, along with a large reasoning-annotated dataset and reinforcement learning training.

Findings

Achieves state-of-the-art results on multiple navigation benchmarks.

Successfully transfers to real-world robotic platforms zero-shot.

Demonstrates improved reasoning and memory capabilities in navigation tasks.

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.