Follow-Bench 2.0: An End-to-End 3D Benchmark for Socially-Aware Robot Person Following

Socially-aware robot person following (RPF) requires a mobile robot to follow a designated person in dynamic human environments while maintaining target identity, avoiding obstacles and pedestrians, and preserving socially comfortable formations. Existing benchmarks only partially evaluate this coupled problem: embodied visual tracking benchmarks emphasize target visibility with limited social interaction, person re-identification benchmarks study perception without closed-loop control, and motion-planning benchmarks often assume ground-truth target and pedestrian states. We introduce Follow-Bench 2.0, an end-to-end 3D benchmark for evaluating socially-aware RPF under coupled perception--planning challenges. Built in Unreal Engine, Follow-Bench 2.0 provides difficulty-leveled scenarios with diverse pedestrian flows, lighting and weather conditions, cluttered layouts, bottlenecks, queueing behaviors, distractors, and temporary target occlusions. The benchmark supports modular perception--planning pipelines, end-to-end visual policies, and foundation-model-based active trackers under a unified closed-loop protocol. It further evaluates both back- and side-following configurations and reports safety--comfort metrics covering task success, target visibility, target recovery, social-space intrusion, following formation, and motion smoothness. Experiments show that RPF-oriented modular pipelines substantially outperform current end-to-end visual policies on close and socially comfortable following, but also reveal that socially-aware RPF remains far from solved in interaction-heavy environments and especially in side-following. Follow-Bench 2.0 therefore provides a diagnostic platform for studying how perception errors, occlusions, crowd interactions, and following configurations jointly affect robot person following. Our code is available at https://anonymous.4open.science/r/follow-benchv2-NIPS2026-03D3/.