Learning Crowd-Aware Robot Navigation from Challenging Environments via Distributed Deep Reinforcement Learning

This paper presents a deep reinforcement learning (DRL) sframework for safe and efficient navigation in crowded environments. Here, the robot learns cooperative behavior using a new reward function that penalizes robot actions interfering with the pedestrian's movement. Also, we propose a simulated pedestrian policy reflecting data from actual pedestrian movements. Furthermore, we introduce a collision detection that considers the pedestrian's personal space to generate affinity robot behavior. To efficiently explore this simulation environment, we propose distributed learning using Ape-X [1]. We deployed the robot in a real environment and verified its crowd-aware navigation performance compared with an actual human in terms of path length, travel time, and the number of abrupt avoidances.