机器人根据人类行为调整故障解释，减少误解，提升协作效率。
Adapting Robot's Explanation for Failures Based on Observed Human Behavior in Human-Robot Collaboration
- 通过分析人脸表情、眼神和手势预测用户困惑
- 在55人实验中验证了行为变化与解释需求的相关性
- 可实时适配解释层级，适合人机协作场景
本研究旨在通过解读人类行为，预判机器人提供故障解释时可能引发的用户困惑，从而让机器人动态调整解释方式，实现更自然高效的协作。基于包含55名参与者面部情绪检测、眼动追踪与手势数据的用户研究数据集，我们分析了不同故障类型及解释程度下人类行为的变化。目标是评估用户是否已准备好接受较简略的解释而不产生困惑。研究提出一个数据驱动的混淆预测模型，并设计了一种基于该模型的自适应解释机制。评估结果表明，该方法具有显著潜力，可有效提升人机协作体验。
原文摘要 · Abstract (English)
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.
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