A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation has advanced tremendously in recent years, the fair evaluation of algorithms that tackle social navigation remains hard because it involves not just robotic agents moving in static environments but also dynamic human agents and their perceptions of the appropriateness of robot behavior. In contrast, clear, repeatable, and accessible benchmarks have accelerated progress in fields like computer vision, natural language processing and traditional robot navigation by enabling researchers to fairly compare algorithms, revealing limitations of existing solutions and illuminating promising new directions. We believe the same approach can benefit social navigation. In this paper, we pave the road towards common, widely accessible, and repeatable benchmarking criteria to evaluate social robot navigation. Our contributions include (a) a definition of a socially navigating robot as one that respects the principles of safety, comfort, legibility, politeness, social competency, agent understanding, proactivity, and responsiveness to context, (b) guidelines for the use of metrics, development of scenarios, benchmarks, datasets, and simulators to evaluate social navigation, and (c) a design of a social navigation metrics framework to make it easier to compare results from different simulators, robots and datasets.

翻译：将机器人广泛部署于人类环境的主要挑战之一是在人群中导航，通常称为社交机器人导航。尽管近年来社交导航领域取得了巨大进展，但公平评估处理社交导航的算法仍然困难，因为这不仅涉及在静态环境中移动的机器人主体，还涉及动态的人类主体及其对机器人行为恰当性的感知。相比之下，清晰、可重复且易于获取的基准测试——如计算机视觉、自然语言处理和传统机器人导航领域——通过使研究人员能够公平比较算法、揭示现有解决方案的局限性并指明有前景的新方向，推动了这些领域的进步。我们相信同样的方法也能惠及社交导航。在本文中，我们为建立通用、广泛可获取且可重复的基准测试标准以评估社交机器人导航铺平道路。我们的贡献包括：(a) 将社交导航机器人定义为遵循安全、舒适、可读性、礼貌、社交能力、主体理解、主动性及对情境响应原则的机器人；(b) 制定使用度量标准、开发场景、基准测试、数据集和模拟器以评估社交导航的指南；(c) 设计一个社交导航度量框架，以便更轻松地比较不同模拟器、机器人和数据集的结果。