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.
@article{arxiv.2509.01251,
title = {Towards Data-Driven Metrics for Social Robot Navigation Benchmarking},
author = {Pilar Bachiller-Burgos and Ulysses Bernardet and Luis V. Calderita and Pranup Chhetri and Anthony Francis and Noriaki Hirose and Noé Pérez and Dhruv Shah and Phani T. Singamaneni and Xuesu Xiao and Luis J. Manso},
journal= {arXiv preprint arXiv:2509.01251},
year = {2026}
}