对比量化指标与人工评估，找出行之有效的机器人导航评测方法
Metrics vs Surveys: An Analysis for Human-Aligned Benchmarking in Social Robot Navigation
- 分析量化指标与人类主观评价的相关性，探索可替代调查的评测方式
- 发现现有指标仅部分反映人类感知，关键主观因素仍被忽略
- 适合需要快速评估机器人交互表现的研究者和开发者参考
社交导航（social navigation）是将移动机器人融入人类环境的关键挑战。其评估复杂，需兼顾舒适性、安全性与可理解性等多方面因素。当前主流的人类中心评估依赖问卷调查，虽可靠但成本高、难复现、难以跨系统比较。相比之下，数值化社会导航指标计算便捷，利于系统间对比，但学界尚未形成统一标准。本文探讨量化指标与人类评估之间的关联，旨在识别是否某些可量化的测量能反映人类感知。若存在相关性，这些指标可作为大规模调查不可行时的初步基准工具。结果表明，尽管现有指标捕捉了部分导航行为特征，但仍无法充分涵盖重要主观维度，亟需开发新指标以实现更符合人类偏好的评估体系。
原文摘要 · Abstract (English)
Social, also called human-aware, navigation is a key challenge for integrating mobile robots into human environments. The evaluation of such systems is complex, as factors such as comfort, safety, and legibility must be considered. Human-centered assessments, typically conducted through surveys, provide reliable insights but are costly, resource-intensive, and difficult to reproduce or compare across systems. Alternatively, numerical social navigation metrics are easy to compute and facilitate comparisons, yet the community lacks consensus on a standard set of metrics. This work explores the relationship between numerical metrics and human-centered evaluations to identify potential correlations. If specific quantitative measures align with human perceptions, they could serve as preliminary benchmarking tools, providing a human-aligned assessment when large-scale surveys are not feasible. Our results indicate that while current metrics capture some aspects of robot navigation behavior, important subjective factors remain insufficiently represented, necessitating new metrics.
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