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.
@article{arxiv.1810.00663,
title = {Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation},
author = {Xiaoxue Zang and Ashwini Pokle and Marynel Vázquez and Kevin Chen and Juan Carlos Niebles and Alvaro Soto and Silvio Savarese},
journal= {arXiv preprint arXiv:1810.00663},
year = {2018}
}