Nav-R1: Reasoning and Navigation in Embodied Scenes
Authors: name: Qingxiang Liu, name: Ting Huang, name: Zeyu Zhang, name: Hao Tang
Organization: Peking University

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.