# Nav-R1: Reasoning and Navigation in Embodied Scenes

arXiv: https://arxiv.org/abs/2509.10884 · HTML: https://mlsanity.com/paper/2509.10884/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2025.

## Built on by (most cited first, 30 of 38)

- [VLA-R1: Enhancing Reasoning in Vision-Language-Action Models](https://mlsanity.com/paper/2510.01623/) (2025)
- [A Survey on Agentic Multimodal Large Language Models](https://mlsanity.com/paper/2510.10991/) (2025)
- [NavForesee: A Unified Vision-Language World Model for Hierarchical Planning and Dual-Horizon Navigation Prediction](https://mlsanity.com/paper/2512.01550/) (2025)
- [EvoVLA: Self-Evolving Vision-Language-Action Model](https://mlsanity.com/paper/2511.16166/) (2025)
- [Nav-R2 Dual-Relation Reasoning for Generalizable Open-Vocabulary Object-Goal Navigation](https://mlsanity.com/paper/2512.02400/) (2025)
- [VLingNav: Embodied Navigation with Adaptive Reasoning and Visual-Assisted Linguistic Memory](https://mlsanity.com/paper/2601.08665/) (2026)
- [AwareVLN: Reasoning with Self-awareness for Vision-Language Navigation](https://mlsanity.com/paper/2605.22816/) (2026)
- [StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes](https://mlsanity.com/paper/2509.16415/) (2025)
- [Beyond Flatlands: Unlocking Spatial Intelligence by Decoupling 3D Reasoning from Numerical Regression](https://mlsanity.com/paper/2511.11239/) (2025)
- [PanoEnv: Exploring 3D Spatial Intelligence in Panoramic Environments with Reinforcement Learning](https://mlsanity.com/paper/2602.21992/) (2026)
- [MWM: Mobile World Models for Action-Conditioned Consistent Prediction](https://mlsanity.com/paper/2603.07799/) (2026)
- [VaseVQA: Multimodal Agent and Benchmark for Ancient Greek Pottery](https://mlsanity.com/paper/2509.17191/) (2025)
- [GeoWorld: Geometric World Models](https://mlsanity.com/paper/2602.23058/) (2026)
- [EgoLCD: Egocentric Video Generation with Long Context Diffusion](https://mlsanity.com/paper/2512.04515/) (2025)
- [SocialNav-MoE: A Mixture-of-Experts Vision Language Model for Socially Compliant Navigation with Reinforcement Fine-Tuning](https://mlsanity.com/paper/2512.14757/) (2025)
- [AnyDepth: Depth Estimation Made Easy](https://mlsanity.com/paper/2601.02760/) (2026)
- [LongNav-R1: Horizon-Adaptive Multi-Turn RL for Long-Horizon VLA Navigation](https://mlsanity.com/paper/2602.12351/) (2026)
- [CoMaTrack: Competitive Multi-Agent Game-Theoretic Tracking with Vision-Language-Action Models](https://mlsanity.com/paper/2603.22846/) (2026)
- [VaseVQA-3D: Benchmarking 3D VLMs on Ancient Greek Pottery](https://mlsanity.com/paper/2510.04479/) (2025)
- [MapDream: Task-Driven Map Learning for Vision-Language Navigation](https://mlsanity.com/paper/2602.00222/) (2026)
- [ProFocus: Proactive Perception and Focused Reasoning in Vision-and-Language Navigation](https://mlsanity.com/paper/2603.05530/) (2026)
- [SeeNav-Agent: Enhancing Vision-Language Navigation with Visual Prompt and Step-Level Policy Optimization](https://mlsanity.com/paper/2512.02631/) (2025)
- [3D CoCa v2: Contrastive Learners with Test-Time Search for Generalizable Spatial Intelligence](https://mlsanity.com/paper/2601.06496/) (2026)
- [Dense Reward for Multi-View 3D Reasoning with Global Maps and Local Views](https://mlsanity.com/paper/2606.23557/) (2026)
- [MerNav: A Highly Generalizable Memory-Execute-Review Framework for Zero-Shot Object Goal Navigation](https://mlsanity.com/paper/2602.05467/) (2026)
- [What if? Emulative Simulation with World Models for Situated Reasoning](https://mlsanity.com/paper/2603.06445/) (2026)
- [UniMesh: Unifying 3D Mesh Understanding and Generation](https://mlsanity.com/paper/2604.17472/) (2026)
- [Goal2Pixel: Grounding Goals to Pixels for Vision-Language Navigation](https://mlsanity.com/paper/2606.01621/) (2026)
- [ConsiSpace: Learning Geometric Consistency Matters for Video Spatial Reasoning](https://mlsanity.com/paper/2607.17599/) (2026)
- [LightNav-0: Eliciting VLM Spatial Intelligence for Generalist Embodied Navigation](https://mlsanity.com/paper/2608.30935/) (2026)

## Abstract (arXiv)

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.