Abstract
This work aims to interpret human behavior to anticipate potential user confusion when a robot provides explanations for failure, allowing the robot to adapt its explanations for more natural and efficient collaboration. Using a dataset that included facial emotion detection, eye gaze estimation, and gestures from 55 participants in a user study, we analyzed how human behavior changed in response to different types of failures and varying explanation levels. Our goal is to assess whether human collaborators are ready to accept less detailed explanations without inducing confusion. We formulate a data-driven predictor to predict human confusion during robot failure explanations. We also propose and evaluate a mechanism, based on the predictor, to adapt the explanation level according to observed human behavior. The promising results from this evaluation indicate the potential of this research in adapting a robot's explanations for failures to enhance the collaborative experience.
Abstract (translated)
这项工作旨在解读人类行为，以便在机器人提供故障解释时预测潜在的用户困惑，从而使机器人能够根据情况调整其解释方式，以实现更自然和高效的协作。我们使用了一个数据集，该数据集中包含了55名参与者在用户研究中表现出的面部表情检测、目光估计以及手势等信息，分析了人类行为如何随着不同类型故障及不同解释水平的变化而变化。我们的目标是评估人类合作者是否能够在不引起困惑的情况下接受更简洁的解释。 我们制定了一个基于数据驱动的方法来预测机器人在提供故障解释过程中的人类困惑程度，并提出了并评估了一种机制，该机制可以根据观察到的人类行为调整解释水平。这项研究的初步结果表明，根据人类的行为适应机器人的解释方式具有增强协作体验的潜力。
URL
https://arxiv.org/abs/2504.09717
