用用户注视行为实时检测机器人协作中的执行与决策故障
Real-Time Detection of Robot Failures Using Gaze Dynamics in Collaborative Tasks
- 通过眼动数据提取注视模式，训练机器学习模型识别故障
- 前5秒内对执行类故障识别率达90%，决策类达80%
- 适合关注人机协作安全与交互设计的研究者
在人机协作任务中实时检测机器人故障对维持信任至关重要。本研究探索用户注视行为作为故障指示器的潜力，利用机器学习模型区分无故障与两类故障：执行类与决策类。26名参与者在协作完成拼图任务时，其眼动数据被采集。基于平均注视转移速率、特定兴趣区域注视概率等眼动指标，训练了随机森林、AdaBoost、XGBoost、SVM和CatBoost等分类器。结果显示，随机森林在故障发生后前5秒内对执行类故障检测准确率达90%，对决策类故障达80%。通过将眼动数据分段为3、5、10秒间隔进行实时检测评估，验证了该方法的可行性。研究表明，注视动态可有效用于人机协作中的实时错误检测。
原文摘要 · Abstract (English)
Detecting robot failures during collaborative tasks is crucial for maintaining trust in human-robot interactions. This study investigates user gaze behaviour as an indicator of robot failures, utilising machine learning models to distinguish between non-failure and two types of failures: executional and decisional. Eye-tracking data were collected from 26 participants collaborating with a robot on Tangram puzzle-solving tasks. Gaze metrics, such as average gaze shift rates and the probability of gazing at specific areas of interest, were used to train machine learning classifiers, including Random Forest, AdaBoost, XGBoost, SVM, and CatBoost. The results show that Random Forest achieved 90% accuracy for detecting executional failures and 80% for decisional failures using the first 5 seconds of failure data. Real-time failure detection was evaluated by segmenting gaze data into intervals of 3, 5, and 10 seconds. These findings highlight the potential of gaze dynamics for real-time error detection in human-robot collaboration.
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