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

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.

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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