梳理2020-2025年社交机器人导航评估方法，揭示领域标准缺失问题
A short methodological review on social robot navigation benchmarking
- 聚焦2020至2025年间85篇论文，系统分析评估指标与算法
- 指出当前缺乏统一评估标准，易导致研究结论矛盾
- 适合关注机器人人机交互评估的科研人员参考
社交机器人导航能力使机器人能在有人环境中安全、高效地移动，同时保障人类的安全感与信任。然而，该领域尚未形成统一的评估标准。这一缺失可能阻碍研究进展，并导致相互矛盾的结论。为填补此空白，本文对2020年1月至2025年7月期间发表的130篇相关论文进行了筛选，最终分析了85篇符合标准的研究。文章系统梳理了文献中采用的评估指标、基准测试所用算法、人类问卷调查的应用方式，以及如何基于基准结果得出结论，旨在推动该领域评估方法的规范化。
原文摘要 · Abstract (English)
Social Robot Navigation is the skill that allows robots to move efficiently in human-populated environments while ensuring safety, comfort, and trust. Unlike other areas of research, the scientific community has not yet achieved an agreement on how Social Robot Navigation should be benchmarked. This is notably important, as the lack of a de facto standard to benchmark Social Robot Navigation can hinder the progress of the field and may lead to contradicting conclusions. Motivated by this gap, we contribute with a short review focused exclusively on benchmarking trends in the period from January 2020 to July 2025. Of the 130 papers identified by our search using IEEE Xplore, we analysed the 85 papers that met the criteria of the review. This review addresses the metrics used in the literature for benchmarking purposes, the algorithms employed in such benchmarks, the use of human surveys for benchmarking, and how conclusions are drawn from the benchmarking results, when applicable.
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