COACH: Cooperative Robot Teaching
Cunjun Yu, Yiqing Xu, Linfeng Li, David Hsu

TL;DR
This paper introduces COACH, a framework for cooperative robot teaching modeled as a Markov game, enabling robots to effectively teach physical tasks through interactive learning, demonstrated in simulation and real-world experiments.
Contribution
It formalizes cooperative robot teaching as a Markov game and provides an efficient approximate solution for interactive robot teaching tasks.
Findings
Successful application in simulated video game task
Effective real robot teaching demonstration
Framework enables scalable robot teaching
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsReinforcement Learning in Robotics · Intelligent Tutoring Systems and Adaptive Learning · Robot Manipulation and Learning
