This paper presents a joint effort towards the development of a data-driven Social Robot Navigation metric to facilitate benchmarking and policy optimization for ground robots. We compiled a dataset with 4427 trajectories -- 182 real and 4245 simulated -- and presented it to human raters, yielding a total of 4402 rated trajectories after data quality assurance. Notably, we provide the first all-encompassing learned social robot navigation metric, along qualitative and quantitative results, including the test loss achieved, a comparison against hand-crafted metrics, and an ablation study. All data, software, and model weights are publicly available.
翻译：本文提出了一项联合研究，旨在开发一种数据驱动的社交机器人导航指标，以促进地面机器人的基准测试与策略优化。我们构建了一个包含4427条轨迹的数据集（其中182条为真实轨迹，4245条为仿真轨迹），并邀请人类评估者对其进行评分，经过数据质量保证后，最终获得4402条已评分轨迹。值得注意的是，我们首次提出了一个全面的学习型社交机器人导航指标，并提供了定性与定量分析结果，包括所达到的测试损失、与人工设计指标的对比以及消融实验。所有数据、软件及模型权重均已公开提供。
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