Danger-zone and maximum entropy deep inverse reinforcement learning for human-robot navigation

Abstract

As a result of the development of artificial intelligence in recent years, scientists have gone further in the field of human-robot interaction (HRI), and one of the remaining problems is how to achieve a safe and human comfort-satisfying navigation when it comes to crowd-robot interaction (CRI). This article explores two existing deep learning reinforcement (DRL) methods, Danger-zone (DZ) and Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL). The former method proposes a Danger-Zone to predict the trajectory of pedestrians, using the DRL network to achieve obstacle avoidance and combining advanced algorithms with it to summarize the most effective combination. The latter method collects expert demonstrations in an uncontrolled environment. It uses DNN networks to predict human behavior and compares the results with the actual trajectories to demonstrate their validity. This article summarizes and evaluates these two methods. Moreover, this article also gives various outlooks on the direction of human-computer interaction.