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 Proximity-Aware Tasks (referred as to Risk and Proximity 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：在遮挡且空间受限的室内环境中学习如何在人群中导航，是具身智能体融入人类社会所需的关键能力。本文提出了一种端到端架构，通过利用邻近感知任务（称为风险与邻近指南针）将常识性社交行为推理能力注入到强化学习导航策略中。为此，我们的任务利用了即时和未来的碰撞危险概念。此外，我们专门为模拟环境中的社交导航任务设计了一种评估协议，通过分析人机空间交互的最小单元——即"遭遇"事件，来捕获策略的细粒度特征与特性。我们在Gibson4+和Habitat-Matterport3D数据集上验证了本方法的有效性。