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.

翻译：人机交互研究的一个关键领域是解决人机对应问题，即当人类与机器人具有不同的动力学和运动学结构时，机器人如何学习复现人类的运动示范。评估这些对应问题解决方案通常需要使用定性调查方法，而这些方法的设计与实施往往耗时费力。此外，定性调查结果会因参与调查人群的差异而产生波动。本文提出采用异构时间序列相似性度量作为评估运动对应性的定量评价指标，以补充现有定性调查方法的不足。为验证这些度量方法的适用性，我们开发了基于行为克隆的运动对应模型，并通过定性调查与定量度量相结合的方式对其进行评估。通过将计算得到的相似性评分与人类调查结果进行对比，我们发现Gromov动态时间规整算法是一种具有潜力的运动对应性定量评估度量方法。