{"ID":2875807,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.01251","arxiv_id":"2509.01251","title":"Towards Data-Driven Metrics for Social Robot Navigation Benchmarking","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.01251","url_pdf":"https://arxiv.org/pdf/2509.01251v2","authors":"[\"Pilar Bachiller-Burgos\",\"Ulysses Bernardet\",\"Luis V. Calderita\",\"Pranup Chhetri\",\"Anthony Francis\",\"Noriaki Hirose\",\"Noé Pérez\",\"Dhruv Shah\",\"Phani T. Singamaneni\",\"Xuesu Xiao\",\"Luis J. Manso\"]","published":"2025-09-01T08:42:28Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}