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
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/D18-1286.pdf