Nav-R1: Reasoning and Navigation in Embodied Scenes
Qingxiang Liu, Ting Huang, Zeyu Zhang, Hao Tang
2025-09-16
Summary
This paper introduces Nav-R1, a new AI model designed to help robots navigate complex 3D environments like real-world buildings or simulated spaces. It aims to make robot navigation more reliable and efficient by improving how the robot 'thinks' and plans its movements.
What's the problem?
Current robots struggle with navigating because their reasoning process can be messy and inconsistent, making it hard for them to adapt to new places. They also have trouble balancing the need to think ahead and plan a route with the need to react quickly to obstacles and changes in their surroundings. Essentially, they can't both plan a smart path *and* move smoothly in real-time.
What's the solution?
The researchers created Nav-R1 by first building a huge dataset of step-by-step instructions for navigating, kind of like giving the robot a lot of examples of how to think through a route. Then, they used a special learning technique called reinforcement learning, rewarding the robot for following a clear thought process, understanding its environment, and actually reaching its destination. Finally, they designed a system where the robot can separate careful planning from quick reactions, allowing it to think strategically while still avoiding obstacles.
Why it matters?
This work is important because it represents a step towards more capable and reliable robots. By improving a robot’s ability to navigate, we can unlock applications in areas like delivery services, search and rescue, and even helping people with disabilities. The fact that it works well even on a real robot with limited computing power shows it has potential for practical use.
Abstract
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.