Learning how to navigate among humans in an occluded and spatially constrained indoor environment, is a key ability required to embodied agent to be integrated into our society. In this paper, we propose an end-to-end architecture that exploits Socially-Aware Tasks (referred as to Risk and Social Compass) to inject into a reinforcement learning navigation policy the ability to infer common-sense social behaviors. To this end, our tasks exploit the notion of immediate and future dangers of collision. Furthermore, we propose an evaluation protocol specifically designed for the Social Navigation Task in simulated environments. This is done to capture fine-grained features and characteristics of the policy by analyzing the minimal unit of human-robot spatial interaction, called Encounter. We validate our approach on Gibson4+ and Habitat-Matterport3D datasets.
翻译:学习如何在隐蔽和空间受限制的室内环境中在人类中航行,这是将代理人纳入我们社会的关键能力。在本文件中,我们提议一个端对端结构,利用社会软件任务(称为风险和社会指南),将推导常识社会行为的能力引入强化学习导航政策。为此目的,我们的任务利用了碰撞的近期和今后危险的概念。此外,我们提议了一项专门为模拟环境中的社会导航任务设计的评估协议。这样做的目的是通过分析人类-机器人空间互动的最小单位(称为Encounter)来捕捉该政策精细的特征和特征。我们验证了我们对Gibet4+ 和Homen-Metport3D 数据集的做法。