用动态时间对齐方法量化人机动作对应度，替代耗时的主观评估。
Assessing Similarity Measures for the Evaluation of Human-Robot Motion Correspondence
- 引入异构时间序列相似性度量，实现动作对应性的定量评估。
- 对比实验显示Gromov动态时间对齐与人工评分高度一致。
- 适合关注人机交互动作模仿效果评估的研究者使用。
人机交互研究中的核心问题之一是解决人机动作对应性问题，即当人类与机器人具有不同动力学和运动结构时，如何让机器人学习并复现人类的动作示范。现有评估方法多依赖主观问卷，设计耗时且结果受受访者群体影响。本文提出使用异构时间序列相似性度量作为量化评价指标，以补充传统主观评估。为此，我们构建基于行为克隆的动作对应模型，并结合主观问卷与多种定量度量进行评估。通过将相似性得分与人工调查结果对比，发现Gromov动态时间对齐（Gromov Dynamic Time Warping）在预测人类感知一致性方面表现优异，具备成为可靠量化指标的潜力。
原文摘要 · Abstract (English)
One key area of research in Human-Robot Interaction is solving the human-robot correspondence problem, which asks how a robot can learn to reproduce a human motion demonstration when the human and robot have different dynamics and kinematic structures. Evaluating these correspondence problem solutions often requires the use of qualitative surveys that can be time consuming to design and administer. Additionally, qualitative survey results vary depending on the population of survey participants. In this paper, we propose the use of heterogeneous time-series similarity measures as a quantitative evaluation metric for evaluating motion correspondence to complement these qualitative surveys. To assess the suitability of these measures, we develop a behavioral cloning-based motion correspondence model, and evaluate it with a qualitative survey as well as quantitative measures. By comparing the resulting similarity scores with the human survey results, we identify Gromov Dynamic Time Warping as a promising quantitative measure for evaluating motion correspondence.
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