Socially aware robot navigation in crowds via deep reinforcement learning with resilient reward functions

Abstract

Robots navigating in a robot–human coexisting environment need to optimize their paths not only for task-related performance (e.g. safety and efficiency) but also for their social compliance to other pedestrians. This is a crucial yet challenging task. Previous work has shown the power of deep reinforcement learning (DRL) techniques by employing them to train efficient policies for robot navigation. However, their performance deteriorates when the crowd size grows. We cope with this problem by allowing the robot to keep an adapting distance from the pedestrians and perform safe navigation even in high density environments. We first derive a quantitative formula representing the relationship between uncomfortable distance and pedestrian density from a real-word tracking dataset. Then this formula is applied in reward shaping of DRL to get resilient reward functions (R2F). Qualitative and quantitative evaluation results demonstrate that our method outperforms state-of-the-art methods in both low and high pedestrian density environments.