Motor skills, especially fine motor skills like handwriting, play an
essential role in academic pursuits and everyday life. Traditional methods to
teach these skills, although effective, can be time-consuming and inconsistent.
With the rise of advanced technologies like robotics and artificial
intelligence, there is increasing interest in automating such teaching
processes using these technologies, via human-robot and human-computer
interactions. In this study, we examine the potential of a virtual AI teacher
in emulating the techniques of human educators for motor skill acquisition. We
introduce an AI teacher model that captures the distinct characteristics of
human instructors. Using a Reinforcement Learning environment tailored to mimic
teacher-learner interactions, we tested our AI model against four guiding
hypotheses, emphasizing improved learner performance, enhanced rate of skill
acquisition, and reduced variability in learning outcomes. Our findings,
validated on synthetic learners, revealed significant improvements across all
tested hypotheses. Notably, our model showcased robustness across different
learners and settings and demonstrated adaptability to handwriting. This
research underscores the potential of integrating Reinforcement Learning and
Imitation Learning models with robotics in revolutionizing the teaching of
critical motor skills.

本研究探讨了一种虚拟人工智能教师模型，通过模拟人类教育者的技巧来提高学习者的运动技能，研究结果表明，该模型在不同学习者和环境下都展现出鲁棒性，并且对手写具有适应性，强调了集成强化学习和模仿学习模型与机器人技术，在革新关键运动技能教学方面的潜力。