Knowledge and skills can transfer from human teachers to human students. However, such direct transfer is often not scalable for physical tasks, as they require one-to-one interaction, and human teachers are not available in sufficient numbers. Machine learning enables robots to become experts and play the role of teachers to help in this situation. In this work, we formalize cooperative robot teaching as a Markov game, consisting of four key elements: the target task, the student model, the teacher model, and the interactive teaching-learning process. Under a moderate assumption, the Markov game reduces to a partially observable Markov decision process, with an efficient approximate solution. We illustrate our approach on two cooperative tasks, one in a simulated video game and one with a real robot.

翻译：摘要：知识与技能可以从人类教师传递至人类学生。然而，对于物理任务而言，这种直接传递通常难以规模化，因为它需要一对一交互，且人类教师数量不足以满足需求。机器学习使机器人能够成为专家并扮演教师角色以应对这一情况。在本研究中，我们将协同机器人教学形式化为一个马尔可夫博弈，包含四个关键要素：目标任务、学生模型、教师模型以及交互式教学-学习过程。在适度假设下，该马尔可夫博弈可简化为一个部分可观测的马尔可夫决策过程，并具备高效的近似求解方案。我们通过两项协作任务展示了所提方法：一项在模拟视频游戏中，另一项在真实机器人场景中。