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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.01251 |
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| _version_ | 1866914226351636480 |
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| author | Bachiller-Burgos, Pilar Bernardet, Ulysses Calderita, Luis V. Chhetri, Pranup Francis, Anthony Hirose, Noriaki Pérez, Noé Shah, Dhruv Singamaneni, Phani T. Xiao, Xuesu Manso, Luis J. |
| author_facet | Bachiller-Burgos, Pilar Bernardet, Ulysses Calderita, Luis V. Chhetri, Pranup Francis, Anthony Hirose, Noriaki Pérez, Noé Shah, Dhruv Singamaneni, Phani T. Xiao, Xuesu Manso, Luis J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01251 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Data-Driven Metrics for Social Robot Navigation Benchmarking Bachiller-Burgos, Pilar Bernardet, Ulysses Calderita, Luis V. Chhetri, Pranup Francis, Anthony Hirose, Noriaki Pérez, Noé Shah, Dhruv Singamaneni, Phani T. Xiao, Xuesu Manso, Luis J. Robotics 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. |
| title | Towards Data-Driven Metrics for Social Robot Navigation Benchmarking |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.01251 |