COACH: Cooperative Robot Teaching

Cunjun Yu, Yiqing Xu, Linfeng Li, David Hsu
Proceedings of The 6th Conference on Robot Learning, PMLR 205:1092-1103, 2023.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v205-yu23b, title = {COACH: Cooperative Robot Teaching}, author = {Yu, Cunjun and Xu, Yiqing and Li, Linfeng and Hsu, David}, booktitle = {Proceedings of The 6th Conference on Robot Learning}, pages = {1092--1103}, year = {2023}, editor = {Liu, Karen and Kulic, Dana and Ichnowski, Jeff}, volume = {205}, series = {Proceedings of Machine Learning Research}, month = {14--18 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v205/yu23b/yu23b.pdf}, url = {https://proceedings.mlr.press/v205/yu23b.html}, 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.} }
Endnote
%0 Conference Paper %T COACH: Cooperative Robot Teaching %A Cunjun Yu %A Yiqing Xu %A Linfeng Li %A David Hsu %B Proceedings of The 6th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2023 %E Karen Liu %E Dana Kulic %E Jeff Ichnowski %F pmlr-v205-yu23b %I PMLR %P 1092--1103 %U https://proceedings.mlr.press/v205/yu23b.html %V 205 %X 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.
APA
Yu, C., Xu, Y., Li, L. & Hsu, D.. (2023). COACH: Cooperative Robot Teaching. Proceedings of The 6th Conference on Robot Learning, in Proceedings of Machine Learning Research 205:1092-1103 Available from https://proceedings.mlr.press/v205/yu23b.html.

Related Material