# COACH: Cooperative Robot Teaching

arXiv: https://arxiv.org/abs/2302.06199 · HTML: https://mlsanity.com/paper/2302.06199/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2023.

## Built on by (most cited first, 7 of 7)

- [VAL: Interactive Task Learning with GPT Dialog Parsing](https://mlsanity.com/paper/2310.01627/) (2023)
- [Computational Teaching for Driving via Multi-Task Imitation Learning](https://mlsanity.com/paper/2410.01608/) (2024)
- [Generating Language Corrections for Teaching Physical Control Tasks](https://mlsanity.com/paper/2306.07012/) (2023)
- [Shared Autonomy for Proximal Teaching](https://mlsanity.com/paper/2502.19899/) (2025)
- [CANINE: Coaching Visually Impaired Users for Interactive Navigation with a Robot Guide Dog](https://mlsanity.com/paper/2605.19501/) (2026)
- [Proximal State Nudging: Reducing Skill Atrophy from AI Assistance](https://mlsanity.com/paper/2605.20355/) (2026)
- [Spatio-Temporal Retrieval-based Priors for Adaptive Computational Teaching in Driving](https://mlsanity.com/paper/2606.25224/) (2026)

## Abstract (arXiv)

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.