社会的ロボットナビゲーションシナリオの複雑さの特徴付け
Characterizing the Complexity of Social Robot Navigation Scenarios
社会的ロボットナビゲーションのシナリオ複雑さを規定する要因を整理し、シミュレーションで各種アルゴリズムの性能低下に効く要因（密集・狭さが特に強い）を明らかにした研究。
著者: Andrew Stratton, Kris Hauser, Christoforos Mavrogiannis
分類: cs.RO
原文アブストラクト
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.
