We propose a multi-head attention mechanism as a blending layer in a neural network model that translates natural language to a high level behavioral language for indoor robot navigation. We follow the framework established by (Zang et al., 2018a) that proposes the use of a navigation graph as a knowledge base for the task. Our results show significant performance gains when translating instructions on previously unseen environments, therefore, improving the generalization capabilities of the model.
@article{arxiv.2006.00697,
title = {Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism},
author = {Patricio Cerda-Mardini and Vladimir Araujo and Alvaro Soto},
journal= {arXiv preprint arXiv:2006.00697},
year = {2020}
}