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

TL;DR

This paper introduces an end-to-end deep learning approach that translates natural language navigation instructions into high-level robot navigation plans, leveraging environment maps and attention mechanisms to improve accuracy.

Contribution

The paper presents a novel deep learning model that effectively incorporates environment topology and attention to translate natural language instructions into robot navigation plans.

Findings

Model outperforms baseline methods on a new dataset

Environment maps enhance translation accuracy

Attention mechanisms improve instruction understanding

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.