  The human-robot interaction (HRI) community has developed many methods for
robots to navigate safely and socially alongside humans. However, experimental
procedures to evaluate these works are usually constructed on a per-method
basis. Such disparate evaluations make it difficult to compare the performance
of such methods across the literature. To bridge this gap, we introduce
SocNavBench, a simulation framework for evaluating social navigation
algorithms. SocNavBench comprises a simulator with photo-realistic capabilities
and curated social navigation scenarios grounded in real-world pedestrian data.
We also provide an implementation of a suite of metrics to quantify the
performance of navigation algorithms on these scenarios. Altogether,
SocNavBench provides a test framework for evaluating disparate social
navigation methods in a consistent and interpretable manner. To illustrate its
use, we demonstrate testing three existing social navigation methods and a
baseline method on SocNavBench, showing how the suite of metrics helps infer
their performance trade-offs. Our code is open-source, allowing the addition of
new scenarios and metrics by the community to help evolve SocNavBench to
reflect advancements in our understanding of social navigation.
