Characterizing the Complexity of Social Robot Navigation Scenarios

Andrew Stratton, Kris Hauser, Christoforos Mavrogiannis

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalIEEE Robotics and Automation Letters
DOIs
StateAccepted/In press - 2024
Externally publishedYes

Keywords

  • Autonomous vehicle navigation
  • human-aware motion planning
  • human-centered robotics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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