Nav-R1：具身场景中的推理与导航

摘要

实体导航要求智能体在复杂的三维环境中整合感知、推理与行动，以实现稳健的交互。现有方法常面临推理轨迹不连贯、不稳定等问题，这阻碍了其在多样化环境中的泛化能力，同时难以平衡长时程语义推理与低延迟控制，以满足实时导航需求。为解决这些挑战，我们提出了Nav-R1，一个统一实体环境推理的实体基础模型。首先，我们构建了Nav-CoT-110K，一个包含逐步思维链（CoT）的大规模数据集，专为实体任务设计，支持通过结构化推理进行冷启动初始化。在此基础上，我们设计了一个基于GRPO的强化学习框架，包含格式、理解和导航三种互补奖励机制，以提升结构遵循性、语义基础性和路径保真度。此外，我们引入了“快慢分离”推理范式，将深思熟虑的语义推理与低延迟的响应控制解耦，实现高效且连贯的导航。在实体AI基准测试中的广泛评估表明，Nav-R1在推理与导航性能上平均提升超过8%，持续超越强基线模型。在移动机器人上的实际部署进一步验证了其在有限机载资源下的鲁棒性。代码：https://github.com/AIGeeksGroup/Nav-R1。网站：https://aigeeksgroup.github.io/Nav-R1。

English

Embodied navigation requires agents to integrate perception, reasoning, and action for robust interaction in complex 3D environments. Existing approaches often suffer from incoherent and unstable reasoning traces that hinder generalization across diverse environments, and difficulty balancing long-horizon semantic reasoning with low-latency control for real-time navigation. To address these challenges, we propose Nav-R1, an embodied foundation model that unifies reasoning in embodied environments. We first construct Nav-CoT-110K, a large-scale dataset of step-by-step Chains-of-Thought (CoT) for embodied tasks, which enables cold-start initialization with structured reasoning. Building on this foundation, we design a GRPO-based reinforcement learning framework with three complementary rewards: format, understanding, and navigation, to improve structural adherence, semantic grounding, and path fidelity. Furthermore, we introduce a Fast-in-Slow reasoning paradigm, decoupling deliberate semantic reasoning from low-latency reactive control for efficient yet coherent navigation. Extensive evaluations on embodied AI benchmarks demonstrate that Nav-R1 consistently outperforms strong baselines, with over 8% average improvement in reasoning and navigation performance. Real-world deployment on a mobile robot further validates its robustness under limited onboard resources. Code: https://github.com/AIGeeksGroup/Nav-R1. Website: https://aigeeksgroup.github.io/Nav-R1.

Nav-R1：具身场景中的推理与导航

Nav-R1: Reasoning and Navigation in Embodied Scenes

摘要

Support