Proxemics-based Deep Reinforcement Learning for Robot Navigation in Continuous Action Space

Abstract

This paper presents a deep reinforcement learning approach to learn robot navigation in continuous action space with a motion behavior based on human proxemics. We extended a deep deterministic policy gradient network to include convolutional layers for dealing with motion over multiple timesteps. A proxemics-based cost function for the robot to obtain the desired socially aware navigation behavior was developed and implemented in the learning stage, which respects the personal and intimate space of a human. The performed experiments in the simulated and real environments exhibited the desired behavior. Furthermore, the intrusions into the proxemics zones of a human were significantly reduced compared to similar learned robot navigation approachers.