论文标题
为具体的社会导航利用接近感知的任务
Exploiting Proximity-Aware Tasks for Embodied Social Navigation
论文作者
论文摘要
学习如何在封闭和空间约束的室内环境中在人之间进行导航，这是将代理体现在我们社会中所需的关键能力。在本文中，我们提出了一种端到端的体系结构，该体系结构利用接近感知的任务（称为风险和接近指南针）将其注入强化学习导航政策，以推断常识性的社会行为。为此，我们的任务利用了碰撞的直接和未来危险的概念。此外，我们提出了一个专门为模拟环境中社会导航任务设计的评估协议。这样做是为了通过分析人类机器人空间相互作用的最小单位（称为遭遇）来捕获策略的细粒度和特征。我们验证了我们在Gibson4+和habitat-Matterport3D数据集的方法。
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.
