The human-robot interaction (HRI) community has developed many methods for robots to navigate safely and socially alongside humans. However, experimental procedures to evaluate these works are usually constructed on a per-method basis. Such disparate evaluations make it difficult to compare the performance of such methods across the literature. To bridge this gap, we introduce SocNavBench, a simulation framework for evaluating social navigation algorithms. SocNavBench comprises a simulator with photo-realistic capabilities and curated social navigation scenarios grounded in real-world pedestrian data. We also provide an implementation of a suite of metrics to quantify the performance of navigation algorithms on these scenarios. Altogether, SocNavBench provides a test framework for evaluating disparate social navigation methods in a consistent and interpretable manner. To illustrate its use, we demonstrate testing three existing social navigation methods and a baseline method on SocNavBench, showing how the suite of metrics helps infer their performance trade-offs. Our code is open-source, allowing the addition of new scenarios and metrics by the community to help evolve SocNavBench to reflect advancements in our understanding of social navigation.

中文翻译：

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SocNavBench：用于评估社交导航的基础仿真测试框架

人机交互（HRI）社区开发了许多方法，使机器人可以与人类一起安全和社交地导航。但是，评估这些工作的实验程序通常是基于每个方法构建的。如此不同的评估使得很难在文献中比较此类方法的性能。为了弥合这一差距，我们引入了SocNavBench，这是一种用于评估社交导航算法的仿真框架。SocNavBench包括一个具有照片级逼真的功能的模拟器，并基于真实的行人数据精心策划了社交导航场景。我们还提供了一套衡量指标的实现，以量化这些情况下导航算法的性能。共，SocNavBench提供了一个测试框架，用于以一致且可解释的方式评估不同的社交导航方法。为了说明其用途，我们演示了在SocNavBench上测试三种现有的社交导航方法和一种基准方法，展示了度量标准套件如何帮助推断其性能折衷。我们的代码是开源的，允许社区添加新的场景和指标，以帮助发展SocNavBench以反映我们对社交导航理解的进步。