用行为数据和反馈训练模型，自动预测人机协作中的信任度。
Estimating Trust in Human-Robot Collaboration through Behavioral Indicators and Explainability
- 通过操作员反馈优化机器人路径，生成增强信任的行动策略。
- 机器学习模型识别信任水平准确率达84.07%，AUC-ROC达0.90。
- 适合关注人机信任评估与工业协作安全的研究者与工程师。
工业5.0强调以人为本的人机协作，注重安全、舒适与信任。本研究提出一种数据驱动框架，利用行为指标评估信任水平。框架采用基于偏好的优化算法，根据操作员反馈生成增强信任的轨迹，并以此反馈作为训练标签，构建机器学习模型以预测信任状态。实验在化工场景中进行，机器人协助人类操作员混合化学品。结果表明，机器学习模型分类信任水平准确率超过80%，其中投票分类器达到84.07%准确率，AUC-ROC为0.90。研究证实了数据驱动方法在人机协作中评估信任的有效性，凸显行为指标在预测人类信任动态中的关键作用。
原文摘要 · Abstract (English)
Industry 5.0 focuses on human-centric collaboration between humans and robots, prioritizing safety, comfort, and trust. This study introduces a data-driven framework to assess trust using behavioral indicators. The framework employs a Preference-Based Optimization algorithm to generate trust-enhancing trajectories based on operator feedback. This feedback serves as ground truth for training machine learning models to predict trust levels from behavioral indicators. The framework was tested in a chemical industry scenario where a robot assisted a human operator in mixing chemicals. Machine learning models classified trust with over 80\% accuracy, with the Voting Classifier achieving 84.07\% accuracy and an AUC-ROC score of 0.90. These findings underscore the effectiveness of data-driven methods in assessing trust within human-robot collaboration, emphasizing the valuable role behavioral indicators play in predicting the dynamics of human trust.
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