CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning

TL;DR

This paper introduces CityLearn, a framework with diverse real-world city environments and a compact image representation method that enables sample-efficient navigation policy learning and robust deployment across extreme visual changes.

Contribution

It presents a novel interactive platform and a compact image representation technique that significantly improve sample efficiency and generalization in visual navigation tasks.

Findings

Over 100 city traversals used for training and testing.

Navigation policies trained on a single traversal.

Method is over 100 times faster than using raw images.

Abstract

Visual navigation tasks in real-world environments often require both self-motion and place recognition feedback. While deep reinforcement learning has shown success in solving these perception and decision-making problems in an end-to-end manner, these algorithms require large amounts of experience to learn navigation policies from high-dimensional data, which is generally impractical for real robots due to sample complexity. In this paper, we address these problems with two main contributions. We first leverage place recognition and deep learning techniques combined with goal destination feedback to generate compact, bimodal image representations that can then be used to effectively learn control policies from a small amount of experience. Second, we present an interactive framework, CityLearn, that enables for the first time training and deployment of navigation algorithms across city-sized, realistic environments with extreme visual appearance changes. CityLearn features more than 10 benchmark datasets, often used in visual place recognition and autonomous driving research, including over 100 recorded traversals across 60 cities around the world. We evaluate our approach on two CityLearn environments, training our navigation policy on a single traversal. Results show our method can be over 2 orders of magnitude faster than when using raw images, and can also generalize across extreme visual changes including day to night and summer to winter transitions.