MAC-ID: Multi-Agent Reinforcement Learning with Local Coordination for Individual Diversity

With the increase of robots navigating through crowded environments in our daily lives, the demand for de- signing a socially-aware navigation method considering human- robot interaction has risen. When developing and assessing socially-aware navigation methods, pedestrian motion modeling plays a significant role. However, existing pedestrian models often struggle in complex environments and do not have the capacity to generate diverse pedestrian styles. In this paper, we propose multi-agent reinforcement learning with local coordination for individual diversity (MAC-ID), which can synthesize diverse pedestrian motions via local coordination factor (LCF). Our experiments have demonstrated that the manipulation of the LCF induces interpretable changes in pedestrian behaviors, along with a superior performance compared to existing pedestrian motion models. For evaluating socially-aware navigation methods using MAC-ID, we present a novel benchmark called BSON. It offers realistic and diverse social environments with pedestrians modeled via MAC-ID. We have trained and compared various navigation methods in BSON using a newly proposed metric called socially-aware navigation score (SNS). Through BSON, users can evaluate their socially-aware navigation methods and compare them to baselines.