Characterizing the Complexity of Social Robot Navigation Scenarios

TL;DR

The paper argues for a complexity-based lens to benchmark social robot navigation beyond sparse, cooperative scenarios. It defines contextual and robot-related factors and conducts an extensive simulation study with 500 scenarios per condition to assess how these factors affect a suite of algorithms, including ORCA, SFM, and learning-based controllers such as RGL, MPC-SGAN, and MPPI-SGAN. Key findings show that Density and Environment Geometry have the strongest empirical links to performance, with correlations such as $\rho=-0.878$ and $\rho=-0.760$ for success rate and minimum distance, and $\rho=0.641$ for success as Width increases; high-complexity scenarios also reveal distribution-shift vulnerabilities in data-driven methods. The work highlights the need for high-complexity benchmarks that reflect geometric confinement and mixed-agent interactions to better anticipate real-world robot behavior and reliability.

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.

Figures (3)

• Figure 1: Humans can seamlessly handle a wide range of crowd navigation scenarios dendorfer2019cvpr19trackingdetectionchallenge. In contrast, social robot navigation algorithms struggle to handle scenarios with realistic levels of Complexity trautmanijrr. In this paper, we show that high-Complexity scenarios are underexplored in the literature. We demonstrate principal factors contributing to poor navigation performance, and argue for the definition of benchmarks that account for their impact.
• Figure 2: Left to right: Scenarios of increasing Complexity for each of the Complexity factors considered (b-k). In the leftmost Directionality figure green agents are crossing and blue agents are passing, while different colors represent different policies in Policy Mixture.
• Figure 3: Performance of methods across our experiments. Rows indicate experiments and columns correspond to different evaluation metrics. Each point represents the mean over 500 experiments; shaded regions indicate standard deviation. Mix 1 has 7 SFM and 8 ORCA agents. Mix 2 has 5 SFM, 5 ORCA, 2 CV, and 3 static agents. Mix 3 has 4 SFM, 4 ORCA, 4 CV, 3 static agents.