# Topological Planning with Transformers for Vision-and-Language Navigation

arXiv: https://arxiv.org/abs/2012.05292 · HTML: https://mlsanity.com/paper/2012.05292/

## Key facts

- Cited by 116 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 30 of 116)

- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models](https://mlsanity.com/paper/2305.16986/) (2023)
- [NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation](https://mlsanity.com/paper/2402.15852/) (2024)
- [NaVILA: Legged Robot Vision-Language-Action Model for Navigation](https://mlsanity.com/paper/2412.04453/) (2024)
- [Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigation](https://mlsanity.com/paper/2202.11742/) (2022)
- [ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2304.03047/) (2023)
- [Open-vocabulary Queryable Scene Representations for Real World Planning](https://mlsanity.com/paper/2209.09874/) (2022)
- [Bridging the Gap Between Learning in Discrete and Continuous Environments for Vision-and-Language Navigation](https://mlsanity.com/paper/2203.02764/) (2022)
- [GridMM: Grid Memory Map for Vision-and-Language Navigation](https://mlsanity.com/paper/2307.12907/) (2023)
- [BEVBert: Multimodal Map Pre-training for Language-guided Navigation](https://mlsanity.com/paper/2212.04385/) (2022)
- [Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation](https://mlsanity.com/paper/2109.01115/) (2021)
- [StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context Modeling](https://mlsanity.com/paper/2507.05240/) (2025)
- [Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2203.12667/) (2022)
- [NavGPT-2: Unleashing Navigational Reasoning Capability for Large Vision-Language Models](https://mlsanity.com/paper/2407.12366/) (2024)
- [Language-Aligned Waypoint (LAW) Supervision for Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2109.15207/) (2021)
- [Cross-modal Map Learning for Vision and Language Navigation](https://mlsanity.com/paper/2203.05137/) (2022)
- [LanguageRefer: Spatial-Language Model for 3D Visual Grounding](https://mlsanity.com/paper/2107.03438/) (2021)
- [Sim-2-Sim Transfer for Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2204.09667/) (2022)
- [Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation](https://mlsanity.com/paper/2210.07506/) (2022)
- [MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation](https://mlsanity.com/paper/2401.07314/) (2024)
- [Vision-and-Language Navigation Today and Tomorrow: A Survey in the Era of Foundation Models](https://mlsanity.com/paper/2407.07035/) (2024)
- [Bird's-Eye-View Scene Graph for Vision-Language Navigation](https://mlsanity.com/paper/2308.04758/) (2023)
- [Volumetric Environment Representation for Vision-Language Navigation](https://mlsanity.com/paper/2403.14158/) (2024)
- [VLN-R1: Vision-Language Navigation via Reinforcement Fine-Tuning](https://mlsanity.com/paper/2506.17221/) (2025)
- [A2Nav: Action-Aware Zero-Shot Robot Navigation by Exploiting Vision-and-Language Ability of Foundation Models](https://mlsanity.com/paper/2308.07997/) (2023)
- [Embodied BERT: A Transformer Model for Embodied, Language-guided Visual Task Completion](https://mlsanity.com/paper/2108.04927/) (2021)
- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation](https://mlsanity.com/paper/2211.16649/) (2022)
- [Counterfactual Cycle-Consistent Learning for Instruction Following and Generation in Vision-Language Navigation](https://mlsanity.com/paper/2203.16586/) (2022)

## Abstract (arXiv)

Conventional approaches to vision-and-language navigation (VLN) are trained end-to-end but struggle to perform well in freely traversable environments. Inspired by the robotics community, we propose a modular approach to VLN using topological maps. Given a natural language instruction and topological map, our approach leverages attention mechanisms to predict a navigation plan in the map. The plan is then executed with low-level actions (e.g. forward, rotate) using a robust controller. Experiments show that our method outperforms previous end-to-end approaches, generates interpretable navigation plans, and exhibits intelligent behaviors such as backtracking.