Mobile Robot Navigation in Crowded Environments with Pedestrians Using Meta Reinforcement Learning

Abstract

Mobile robots used in our daily lives must be able to move safely and adaptively in a variety of situations, such as varying numbers of pedestrians, different walking speeds, and varying levels of crowd density. In this paper, we propose crowd-aware robot navigation methods using meta reinforcement learning. We verify whether our methods can adapt to multiple scenario through meta test.