Characterizing the Complexity of Social Robot Navigation Scenarios

TL;DR

This paper investigates the inherent complexity factors in social robot navigation scenarios, highlighting how environmental density and narrowness impact algorithm performance, and advocates for testing in more complex settings.

Contribution

It identifies key complexity factors in social navigation scenarios and empirically demonstrates their effects on algorithm performance, guiding future development and testing.

Findings

Dense and narrow environments significantly reduce navigation performance.

Heterogeneity of agent policies has a lesser impact.

Testing in higher-complexity scenarios is recommended.

Abstract

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.