Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation. Zang, X., Pokle, A., Chen, K., Vazquez, M., Niebles, J., Soto, A., & Savaresse, S. In EMNLP, 2018. ![Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex 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.
@InProceedings{ xiaoxue:etal:2018,
author = {X. Zang and Ashwini Pokle and Kevin Chen and M. Vazquez
and JC. Niebles and A. Soto and S. Savaresse},
title = {Translating Navigation Instructions in Natural Language to
a High-Level Plan for Behavioral Robot Navigation},
booktitle = {EMNLP},
year = {2018},
abstract = {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.},
url = {https://arxiv.org/abs/1810.00663}
}
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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.","url":"https://arxiv.org/abs/1810.00663","bibtex":"@InProceedings{\t xiaoxue:etal:2018,\n author\t= {X. Zang and Ashwini Pokle and Kevin Chen and M. Vazquez\n\t\t and JC. Niebles and A. Soto and S. 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