Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
This addresses the challenge of enabling robots to understand and execute complex navigation commands from humans, representing an incremental improvement in robotic navigation systems.
The paper tackles the problem of translating free-form natural language navigation instructions into high-level plans for robot navigation, using an end-to-end deep learning model with attention mechanisms that incorporate environmental maps, and it significantly outperforms baselines on a new dataset of 10,050 instruction pairs.
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.