# Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation

arXiv: https://arxiv.org/abs/1810.00663 · HTML: https://mlsanity.com/paper/1810.00663/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2018.

## Built on by (most cited first, 9 of 9)

- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Vision-Language Navigation: A Survey and Taxonomy](https://mlsanity.com/paper/2108.11544/) (2021)
- [Ground then Navigate: Language-guided Navigation in Dynamic Scenes](https://mlsanity.com/paper/2209.11972/) (2022)
- [Improving Long-Horizon Imitation Through Instruction Prediction](https://mlsanity.com/paper/2306.12554/) (2023)
- [A modular vision language navigation and manipulation framework for long horizon compositional tasks in indoor environment](https://mlsanity.com/paper/2101.07891/) (2021)
- [Grounding Linguistic Commands to Navigable Regions](https://mlsanity.com/paper/2112.13031/) (2021)
- [RE-MOVE: An Adaptive Policy Design for Robotic Navigation Tasks in Dynamic Environments via Language-Based Feedback](https://mlsanity.com/paper/2303.07622/) (2023)
- [Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech](https://mlsanity.com/paper/2112.13758/) (2021)
- [NSP: A Neuro-Symbolic Natural Language Navigational Planner](https://mlsanity.com/paper/2409.06859/) (2024)

## Abstract (arXiv)

We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation. We use attention models to connect information from both the user instructions and a topological representation of the environment. We evaluate our model's performance on a new dataset containing 10,050 pairs of navigation instructions. Our model significantly outperforms baseline approaches. Furthermore, our results suggest that it is possible to leverage the environment map as a relevant knowledge base to facilitate the translation of free-form navigational instruction.