# PanoVLN: Towards Effective Panoramic Vision-and-Language Navigation > PanoVLN achieves state-of-the-art vision-and-language navigation by pairing panoramic 360-degree inputs with longer action horizons, confidence-guided execution, and geometry-aware visual fusion. - **Source:** [arXiv](https://arxiv.org/abs/2609.34759) - **Published:** 2026-10-01 - **Permalink:** https://picx.dev/p/SGefbV - **Whiteboard:** https://picx.dev/p/SGefbV/image ## Summary ## Summary (Overview) - **PanoVLN** is a novel vision-and-language navigation (VLN) framework that fully exploits panoramic (360°) observations to improve navigation accuracy and efficiency, achieving state-of-the-art results on R2R-CE and RxR-CE benchmarks. - The paper identifies that simply replacing perspective images with panoramas yields limited gains, requiring three key adaptations: **longer action-sequence supervision**, **decision-centric training data construction**, and **geometry-aware visual representation**. - PanoVLN introduces **Confidence-Guided Execution (CGE)**, which dynamically determines how many predicted actions to execute based on action uncertainty, balancing longer-horizon planning with prediction reliability. - With a 4B backbone and RGB-only input, PanoVLN surpasses previous SOTA by **11.9% and 8.7% in success rate** on R2R-CE and RxR-CE Val-Unseen, respectively. - Real-world experiments on a quadruped robot demonstrate **faster navigation with fewer pauses** than prior VLN methods, showing successful transfer from simulation to physical environments. --- ## Introduction and Theoretical Foundation ### Background and Motivation Vision-and-language navigation (VLN) requires an agent to navigate through an environment by following natural-language instructions (Anderson et al., 2018; Krantz et al., 2020). Recent vision-language models (VLMs) have advanced this task by predicting navigation actions from visual observations and instructions. The core motivation of PanoVLN is straightforward: **an equirectangular panorama (ERP) provides a 360° view**, exposing passages, landmarks, and route alternatives across different viewing directions, providing a more complete visual basis for navigation decisions. For example, a panorama can reveal a passage outside a perspective camera's field of view, allowing the model to identify the intended route without additional exploration. ### Key Insight The authors find that **simply replacing perspective images with ERPs under the same training and inference setup does not improve performance**. This observation motivates a detailed diagnostic analysis, leading to three targeted adaptations: 1. **Action Prediction**: Wider visibility supports longer-horizon action planning 2. **Training Supervision**: Branching points are relatively sparse in existing VLN training data, requiring decision-centric data construction 3. **Visual Representation**: Panoramic navigation requires understanding spatial relationships across viewing directions, beyond recognizing individual landmarks --- ## Methodology ### 3.1 Preliminaries **Perspective baseline**: At step $t$, the policy receives a natural-language instruction $x$, the current perspective RGB image $I_t$, and sampled visual history $\mathcal{T}_{