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：将机器人广泛部署到人类活动环境中的一大挑战是在有人群的空间中进行导航，即所谓的社交机器人导航。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重重，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是否恰当的感知。相比之下，清晰、可重复且易于获取的基准测试通过让研究人员公平比较算法、揭示现有解决方案的局限性并指明有前景的新方向，推动了计算机视觉、自然语言处理和传统机器人导航等领域的发展。我们相信同样的方法也能惠及社交导航。本文为建立通用、广泛可及且可重复的基准测试标准以评估社交机器人导航铺平了道路。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、礼貌性、社交能力、主体理解、主动性和情境响应性等原则的机器人；(b) 为使用评估社交导航的度量指标、开发场景、基准测试、数据集和模拟器提供指导；(c) 设计一个社交导航度量框架，以便更轻松地比较来自不同模拟器、机器人和数据集的结果。