With increasing levels of robot autonomy, robots are increasingly being supervised by users with varying levels of robotics expertise. As the diversity of the user population increases, it is important to understand how users with different expertise levels approach the supervision task and how this impacts performance of the human-robot team. This exploratory study investigates how operators with varying expertise levels perceive information and make intervention decisions when supervising a remote robot. We conducted a user study (N=27) where participants supervised a robot autonomously exploring four unknown tunnel environments in a simulator, and provided waypoints to intervene when they believed the robot had encountered difficulties. By analyzing the interaction data and questionnaire responses, we identify differing patterns in intervention timing and decision-making strategies across novice, intermediate, and expert users.

翻译：随着机器人自主性水平的不断提高，机器人正越来越多地由具有不同机器人学专业知识的用户进行监督。随着用户群体的多样性增加，理解不同专业知识水平的用户如何执行监督任务以及这如何影响人机团队的性能变得至关重要。这项探索性研究调查了具有不同专业知识水平的操作员在监督远程机器人时如何感知信息并做出干预决策。我们进行了一项用户研究（N=27），参与者在模拟器中监督一个自主探索四个未知隧道环境的机器人，并在他们认为机器人遇到困难时提供航点进行干预。通过分析交互数据和问卷回答，我们识别了新手、中级和专家用户在干预时机和决策策略方面的不同模式。