RARA: Zero-shot Sim2Real Visual Navigation with Following Foreground Cues

TL;DR

This paper presents RARA, a method enabling zero-shot sim-to-real visual navigation by following foreground cues, using augmentation, regularization, and waypoint abstraction to transfer from simulation to real-world environments.

Contribution

The work introduces a novel approach combining augmentation, regularization, and waypoint-based control to achieve zero-shot sim-to-real transfer for camera-based navigation.

Findings

Successful transfer from simulation to real-world environments

Improved navigation accuracy with foreground cue following

Ablation studies validating the effectiveness of each technique

Abstract

The gap between simulation and the real-world restrains many machine learning breakthroughs in computer vision and reinforcement learning from being applicable in the real world. In this work, we tackle this gap for the specific case of camera-based navigation, formulating it as following a visual cue in the foreground with arbitrary backgrounds. The visual cue in the foreground can often be simulated realistically, such as a line, gate or cone. The challenge then lies in coping with the unknown backgrounds and integrating both. As such, the goal is to train a visual agent on data captured in an empty simulated environment except for this foreground cue and test this model directly in a visually diverse real world. In order to bridge this big gap, we show it's crucial to combine following techniques namely: Randomized augmentation of the fore- and background, regularization with both deep supervision and triplet loss and finally abstraction of the dynamics by using waypoints rather than direct velocity commands. The various techniques are ablated in our experimental results both qualitatively and quantitatively finally demonstrating a successful transfer from simulation to the real world.