Follow-Bench: A Unified Motion Planning Benchmark for Socially-Aware Robot Person Following

Abstract: Robot person following (RPF) -- mobile robots that follow and assist a specific person -- has emerging applications in personal assistance, security patrols, eldercare, and logistics. To be effective, such robots must follow the target while ensuring safety and comfort for both the target and surrounding people. In this work, we present the first end-to-end study of RPF, which (i) surveys representative scenarios, motion-planning methods, and evaluation metrics with a focus on safety and comfort; (ii) introduces Follow-Bench, a unified benchmark simulating diverse scenarios, including various target trajectory patterns, dynamic-crowd flows, and environmental layouts; and (iii) re-implements six popular RPF planners, ensuring that both safety and comfort are systematically considered. Moreover, we evaluate the two highest-performing planners from our benchmark on a differential-drive robot to provide insights into real-world deployment. Extensive simulation and real-world experiments provide quantitative insights into the safety-comfort trade-offs of existing planners, while revealing open challenges and future research directions.