Language-Aligned Waypoint (LAW) Supervision for Vision-and-Language Navigation in Continuous Environments
Sonia Raychaudhuri, Saim Wani, Shivansh Patel, Unnat Jain, Angel Chang
Abstract
In the Vision-and-Language Navigation (VLN) task an embodied agent navigates a 3D environment, following natural language instructions. A challenge in this task is how to handle ‘off the path’ scenarios where an agent veers from a reference path. Prior work supervises the agent with actions based on the shortest path from the agent’s location to the goal, but such goal-oriented supervision is often not in alignment with the instruction. Furthermore, the evaluation metrics employed by prior work do not measure how much of a language instruction the agent is able to follow. In this work, we propose a simple and effective language-aligned supervision scheme, and a new metric that measures the number of sub-instructions the agent has completed during navigation.
BibTeX
@inproceedings{raychaudhuri-etal-2021-language,
title = "Language-Aligned Waypoint ({LAW}) Supervision for Vision-and-Language Navigation in Continuous Environments",
author = "Raychaudhuri, Sonia and
Wani, Saim and
Patel, Shivansh and
Jain, Unnat and
Chang, Angel",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.328/",
doi = "10.18653/v1/2021.emnlp-main.328",
pages = "4018--4028"
}