研究机器人错误如何影响人类教学行为，揭示人机协作中的关键交互机制。
On the Effect of Robot Errors on Human Teaching Dynamics
- 通过用户实验分析机器人错误对教学行为的影响
- 错误越多，人类花时间越长，反馈越详细
- 结果有助于优化人机交互界面与学习算法
人机协同学习在机器人领域日益流行，因其能利用人类对真实任务的经验来促进智能体学习。当人们指导机器人时，会根据机器人表现的变化自然调整教学方式。尽管现有研究多从算法角度整合人类教学动态，但从以人为本的角度理解这些动态仍属未充分探索的基础问题。本文聚焦于一个可能影响教学动态的关键因素——机器人错误。我们开展了一项用户研究，探究机器人错误的存在及其严重程度如何影响三种教学动态维度：反馈粒度、反馈丰富度和教学时长，涵盖强制选择与开放式教学两种情境。结果显示，面对有错误的机器人，人们倾向于花费更长时间教学，在特定轨迹段提供更详细的反馈，且错误会影响教师选择反馈模态。研究为设计高效交互界面及优化算法以更好理解人类意图提供了重要洞见。
原文摘要 · Abstract (English)
Human-in-the-loop learning is gaining popularity, particularly in the field of robotics, because it leverages human knowledge about real-world tasks to facilitate agent learning. When people instruct robots, they naturally adapt their teaching behavior in response to changes in robot performance. While current research predominantly focuses on integrating human teaching dynamics from an algorithmic perspective, understanding these dynamics from a human-centered standpoint is an under-explored, yet fundamental problem. Addressing this issue will enhance both robot learning and user experience. Therefore, this paper explores one potential factor contributing to the dynamic nature of human teaching: robot errors. We conducted a user study to investigate how the presence and severity of robot errors affect three dimensions of human teaching dynamics: feedback granularity, feedback richness, and teaching time, in both forced-choice and open-ended teaching contexts. The results show that people tend to spend more time teaching robots with errors, provide more detailed feedback over specific segments of a robot's trajectory, and that robot error can influence a teacher's choice of feedback modality. Our findings offer valuable insights for designing effective interfaces for interactive learning and optimizing algorithms to better understand human intentions.
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