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：机器人广泛部署的主要挑战之一是在人类密集环境中进行导航，通常称为社交机器人导航。尽管社交导航领域近年来取得了显著进展，但针对解决社交导航问题的算法进行公平评估仍然困难，因为这不仅涉及在静态环境中移动的机器人主体，还包括动态的人类主体及其对机器人行为适当性的感知。相比之下，清晰、可重复且易获取的基准测试推动了计算机视觉、自然语言处理和传统机器人导航等领域的进步，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。我们认为同样的方法能惠及社交导航领域。本文为建立通用、易获取且可重复的社交机器人导航评估标准铺平道路。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、主动性和情境响应性等原则的机器人；(b) 提出关于指标使用、场景开发、基准测试、数据集和模拟器设计的指南，以供评估社交导航；(c) 设计社交导航指标框架，简化不同模拟器、机器人和数据集之间的结果比较。