  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.
