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

Xiaoxue Zang, Ashwini Pokle, Marynel Vázquez, Kevin Chen, Juan Carlos Niebles, Alvaro Soto, Silvio Savarese


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.
Anthology ID:
D18-1286
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2657–2666
Language:
URL:
https://aclanthology.org/D18-1286
DOI:
10.18653/v1/D18-1286
Bibkey:
Cite (ACL):
Xiaoxue Zang, Ashwini Pokle, Marynel Vázquez, Kevin Chen, Juan Carlos Niebles, Alvaro Soto, and Silvio Savarese. 2018. Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2657–2666, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation (Zang et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1286.pdf
Attachment:
 D18-1286.Attachment.zip