研究不同专家水平用户如何监督机器人，发现干预时机和策略差异
Influence of Operator Expertise on Robot Supervision and Intervention
- 通过模拟隧道探索任务，对比新手、中级、专家用户的监督行为
- 专家更早识别风险，干预决策更精准，新手依赖直觉判断
- 结果有助于设计适应用户能力的智能人机协作系统
随着机器人自主性提升，用户对机器人的监督需求也日益增长，而用户在机器人知识上的差异日益显著。本探索性研究考察了不同专业水平的操作者在远程监督机器人时的信息感知方式及干预决策过程。我们开展了一项用户研究（N=27），参与者在模拟器中监督机器人自主探索四个未知隧道环境，并在认为机器人遇到困难时提供航点以干预。通过分析交互数据与问卷反馈，发现新手、中级和专家用户在干预时机和决策策略上存在明显差异。
原文摘要 · Abstract (English)
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.
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