# Cross-Lingual Vision-Language Navigation

arXiv: https://arxiv.org/abs/1910.11301 · HTML: https://mlsanity.com/paper/1910.11301/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2019.

## Built on by (most cited first, 11 of 11)

- [LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action](https://mlsanity.com/paper/2207.04429/) (2022)
- [Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2203.12667/) (2022)
- [Towards Realistic UAV Vision-Language Navigation: Platform, Benchmark, and Methodology](https://mlsanity.com/paper/2410.07087/) (2024)
- [Vision-Language Navigation: A Survey and Taxonomy](https://mlsanity.com/paper/2108.11544/) (2021)
- [Human-Aware Vision-and-Language Navigation: Bridging Simulation to Reality with Dynamic Human Interactions](https://mlsanity.com/paper/2406.19236/) (2024)
- [Meta-Explore: Exploratory Hierarchical Vision-and-Language Navigation Using Scene Object Spectrum Grounding](https://mlsanity.com/paper/2303.04077/) (2023)
- [Vision-Language Navigation with Embodied Intelligence: A Survey](https://mlsanity.com/paper/2402.14304/) (2024)
- [InstruGen: Automatic Instruction Generation for Vision-and-Language Navigation Via Large Multimodal Models](https://mlsanity.com/paper/2411.11394/) (2024)
- [A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation](https://mlsanity.com/paper/2607.09792/) (2026)
- [Accessible Instruction-Following Agent](https://mlsanity.com/paper/2305.06358/) (2023)
- [Breaking Down the Task: A Unit-Grained Hybrid Training Framework for Vision and Language Decision Making](https://mlsanity.com/paper/2307.08016/) (2023)

## Abstract (arXiv)

Commanding a robot to navigate with natural language instructions is a long-term goal for grounded language understanding and robotics. But the dominant language is English, according to previous studies on vision-language navigation (VLN). To go beyond English and serve people speaking different languages, we collect a bilingual Room-to-Room (BL-R2R) dataset, extending the original benchmark with new Chinese instructions. Based on this newly introduced dataset, we study how an agent can be trained on existing English instructions but navigate effectively with another language under a zero-shot learning scenario. Without any training data of the target language, our model shows competitive results even compared to a model with full access to the target language training data. Moreover, we investigate the transferring ability of our model when given a certain amount of target language training data.