Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments
Martin Weiss, Simon Chamorro, Roger Girgis, Margaux Luck, Samira E. Kahou, Joseph P. Cohen, Derek Nowrouzezahrai, Doina Precup
Abstract
Millions of blind and visually-impaired (BVI) people navigate urban environments everyday, using smartphones for high-level path-planning and white canes or guide dogs for local information. However, many BVI people still struggle to travel to new places. In our endeavour to create a navigation assistant for the BVI, we found that existing Reinforcement Learning (RL) environments were unsuitable for the task. This work introduces SEVN, a sidewalk simulation environment and a neural network-based approach to creating a navigation agent. SEVN contains panoramic images with labels for house numbers, doors, and street name signs, and formulations for several navigation tasks. We study the performance of an RL algorithm (PPO) in this setting. Our policy model fuses multi-modal observations in the form of variable resolution images, visible text, and simulated GPS data to navigate to a goal door. We hope that this dataset, simulator, and experimental results will provide a foundation for further research into the creation of agents that can assist members of the BVI community with outdoor navigation.
BibTeX
@inproceedings{corl2019_navigationagents,
title = {Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments},
author = {Martin Weiss and Simon Chamorro and Roger Girgis and Margaux Luck and Samira E. Kahou and Joseph P. Cohen and Derek Nowrouzezahrai and Doina Precup and Florian Golemo and Chris Pal},
booktitle = {CoRL 2019},
year = {2019}
}