Social robot navigation algorithms are often demonstrated in overly simplified scenarios, prohibiting the extraction of practical insights about their relevance to real-world domains. Our key insight is that an understanding of the inherent complexity of a social robot navigation scenario could help characterize the limitations of existing navigation algorithms and provide actionable directions for improvement. Through an exploration of recent literature, we identify a series of factors contributing to the complexity of a scenario, disambiguating between contextual and robot-related ones. We then conduct a simulation study investigating how manipulations of contextual factors impact the performance of a variety of navigation algorithms. We find that dense and narrow environments correlate most strongly with performance drops, while the heterogeneity of agent policies and directionality of interactions have a less pronounced effect. Our findings motivate a shift towards developing and testing algorithms under higher-complexity settings.

翻译：当前社交机器人导航算法常在过于简化的场景中进行演示，这阻碍了从实际应用中提取相关见解以评估其现实适用性。本研究的关键洞见在于：理解社交机器人导航场景的内在复杂性，有助于刻画现有导航算法的局限性，并为改进提供可操作的指导方向。通过对近期文献的梳理，我们识别出一系列影响场景复杂性的因素，并厘清了环境背景因素与机器人自身因素之间的区别。随后，我们通过仿真实验研究了环境背景因素的调整如何影响多种导航算法的性能表现。研究发现，密集且狭窄的环境与性能下降的相关性最为显著，而智能体策略的异质性与交互的方向性所产生的影响相对较弱。这些发现启示我们，应转向在更高复杂度的场景下开展算法的开发与测试工作。