Nav-R1: Reasoning and Navigation in Embodied Scenes
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.
68c8d3b6733e345e52ac1ecb
Qingxiang Liu
68c8d3b6733e345e52ac1ecc
Ting Huang
claimed_verified
2025-09-17T13:05:55.107Z
675aa0331b310ed01068857f
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/PtTMPhuHlfdui_CJOjgHV.png
HuangTing
user
Believe0029
68c8d3b6733e345e52ac1ecd
Zeyu Zhang
claimed_verified
2025-09-16T09:41:48.984Z
64ec877bb93654d4ca5c92e9
https://cdn-avatars.huggingface.co/v1/production/uploads/64ec877bb93654d4ca5c92e9/GvHk_KSdE9Rhnk_o-NaZX.jpeg
Zeyu Zhang
user
SteveZeyuZhang
68c8d3b6733e345e52ac1ece
Hao Tang
Nav-R1: Reasoning and Navigation in Embodied Scenes
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, an embodied foundation model, enhances navigation by integrating structured reasoning and decoupled control mechanisms, outperforming existing approaches on benchmarks and in real-world scenarios.
Embodied navigation Chains-of-Thought (CoT) GRPO-based reinforcement learning Fast-in-Slow reasoning structural adherence semantic grounding path fidelity embodied AI benchmarks mobile robot deployment