# Learning to Stop: A Simple yet Effective Approach to Urban Vision-Language Navigation

arXiv: https://arxiv.org/abs/2009.13112 · HTML: https://mlsanity.com/paper/2009.13112/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 20 of 20)

- [Language Models Meet World Models: Embodied Experiences Enhance Language Models](https://mlsanity.com/paper/2305.10626/) (2023)
- [Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2203.12667/) (2022)
- [VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View](https://mlsanity.com/paper/2307.06082/) (2023)
- [Diagnosing Vision-and-Language Navigation: What Really Matters](https://mlsanity.com/paper/2103.16561/) (2021)
- [Vision-Language Navigation: A Survey and Taxonomy](https://mlsanity.com/paper/2108.11544/) (2021)
- [Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Information](https://mlsanity.com/paper/2104.09580/) (2021)
- [One Step at a Time: Long-Horizon Vision-and-Language Navigation with Milestones](https://mlsanity.com/paper/2202.07028/) (2022)
- [Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor Areas](https://mlsanity.com/paper/2203.13838/) (2022)
- [Ground then Navigate: Language-guided Navigation in Dynamic Scenes](https://mlsanity.com/paper/2209.11972/) (2022)
- [VLN-Video: Utilizing Driving Videos for Outdoor Vision-and-Language Navigation](https://mlsanity.com/paper/2402.03561/) (2024)
- [SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark](https://mlsanity.com/paper/2110.10661/) (2021)
- [Vision-Language Navigation with Embodied Intelligence: A Survey](https://mlsanity.com/paper/2402.14304/) (2024)
- [FedVLN: Privacy-preserving Federated Vision-and-Language Navigation](https://mlsanity.com/paper/2203.14936/) (2022)
- [A Priority Map for Vision-and-Language Navigation with Trajectory Plans and Feature-Location Cues](https://mlsanity.com/paper/2207.11717/) (2022)
- [City-VLM: Towards Multidomain Perception Scene Understanding via Multimodal Incomplete Learning](https://mlsanity.com/paper/2507.12795/) (2025)
- [VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions](https://mlsanity.com/paper/2604.10533/) (2026)
- [GENNAV: Polygon Mask Generation for Generalized Referring Navigable Regions](https://mlsanity.com/paper/2508.21102/) (2025)
- [DreamFly: Causal Memory and Receding-Horizon Diffusion Planning for Aerial Vision-Language Navigation](https://mlsanity.com/paper/2608.12308/) (2026)
- [Temporal-Spatial Object Relations Modeling for Vision-and-Language Navigation](https://mlsanity.com/paper/2403.15691/) (2024)
- [Rule-VLN: Bridging Perception and Compliance via Semantic Reasoning and Geometric Rectification](https://mlsanity.com/paper/2604.16993/) (2026)

## Abstract (arXiv)

Vision-and-Language Navigation (VLN) is a natural language grounding task where an agent learns to follow language instructions and navigate to specified destinations in real-world environments. A key challenge is to recognize and stop at the correct location, especially for complicated outdoor environments. Existing methods treat the STOP action equally as other actions, which results in undesirable behaviors that the agent often fails to stop at the destination even though it might be on the right path. Therefore, we propose Learning to Stop (L2Stop), a simple yet effective policy module that differentiates STOP and other actions. Our approach achieves the new state of the art on a challenging urban VLN dataset Touchdown, outperforming the baseline by 6.89% (absolute improvement) on Success weighted by Edit Distance (SED).