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. This motivates a shift towards developing and testing algorithms under higher-complexity settings.

翻译：社会机器人导航算法通常仅在过度简化的场景中得到验证，这阻碍了对其实际应用价值的实用认知提炼。我们的核心观点是：理解社会机器人导航场景的内在复杂性，有助于揭示现有导航算法的局限性，并为算法改进提供可操作方向。通过系统梳理近年文献，我们识别出影响场景复杂性的系列因素，并区分了情境相关因素与机器人本体因素。随后通过仿真实验，我们探究了情境因素变化对多种导航算法性能的影响。研究发现：密集狭窄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方向的影响相对较弱。这一发现推动了在更高复杂度场景下开发与测试算法的发展方向。