提出新框架评估人机教学中的动态耦合，提升非技术用户教学体验
Bridging Teacher Expectations and Robot Learning via Coupling Dynamics
- 基于人类学习理论构建教学耦合度分类尺度
- 发现耦合程度影响教学效果与教师感知
- 适合人机交互、教育机器人研究者参考
人机教学旨在让非技术人员在机器人部署后根据自身需求进行定制。随着机器学习的发展，人机教学已不再局限于离线学习模式，即人类教师的数据采集与机器人学习阶段分离。近期方法更关注将人类教学与机器人学习过程耦合，这种耦合影响了教学与学习互动的结构、时机和内容。然而，当前尚不明确此类耦合动态如何影响人机教学的有效性及人类对教学过程的感知。本文基于人类学习理论，提出一种新的分类尺度，用于刻画人教师与机器人学习者之间的耦合动态。我们将其应用于部分人机教学文献，识别出耦合动态以及教师心理模型与真实机器人学习系统之间的错位如何影响教学有效性及人类感知。
原文摘要 · Abstract (English)
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
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