  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.
