发表机构
Oakland University（奥克兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于耦合动力学的人机教学交互分类量表，应用于相关文献以探究耦合动力学与教师心智模型不匹配对教学有效性及人类感知的影响。
AI 中文摘要
人机教学旨在让非技术专家在部署后可根据自身需求定制机器人。随着机器学习的最新进展，人机教学不再局限于离线学习，即人类教师的数据收集步骤与机器人学习阶段相互分离。相反，近期人机教学方法更注重将人类教学与机器人学习耦合，这种耦合会影响教学与学习交互的结构、时机和内容。然而，目前尚不清楚这种耦合动力学如何影响人机教学的有效性以及人类对教学过程的感知。本文借鉴人类学习理论，提出一种新量表，用于根据人类教师与机器人学习者之间存在的耦合动力学对人机教学交互进行分类。我们将该量表应用于人机教学文献的一个子集，以识别耦合动力学以及人类教师与真实机器人学习系统的心智模型不匹配如何影响教学有效性和人类对教学过程的感知。
英文摘要
Human-robot teaching focuses on enabling nontechnical experts to customize robots according to their needs after deployment. With recent advances in machine learning, human-robot teaching is no longer confined to offline learning where the data gathering step from a human teacher is separated from when the robot learns. Instead, more recent approaches for human-robot teaching focus on coupling human teaching with robot learning. This coupling impacts the structure, timing, and content of the teaching and learning interaction. However, it is currently unclear how such coupling dynamics affect humanrobot teaching effectiveness and human perceptions towards the teaching process. Informed by human learning theories, in this paper we propose a new scale for classifying human-robot teaching interactions according to coupling dynamics present between the human teacher and robot learner. We apply this scale to a subset of the human-robot teaching literature to identify how coupling dynamics and human teacher mental model mismatches with the ground truth robot learning system affect teaching effectiveness and human perceptions towards the teaching process
Comments8 pages, 1 figure
