Improving Human-AI Coordination through Adversarial Training and Generative Models

Authors: Paresh Chaudhary, Yancheng Liang, Daphne Chen, Simon S. Du, Natasha Jaques

Published: 2025

Publication: ArXiv

The paper introduces GOAT, a novel framework combining pretrained generative models and adversarial training to improve human-AI coordination, achieving state-of-the-art performance on the Overcooked benchmark with real human partners.

Methods: The study utilized a frozen pretrained generative model to simulate cooperative agent policies and applied adversarial training to dynamically generate challenging human-AI interaction scenarios for training.

Key Findings: The effectiveness of GOAT in generalizing human-AI coordination strategies and its performance on the Overcooked benchmark.

Institution: University of Washington

Research Area: Human-AI Coordination, Zero-Shot Coordination, Adversarial Training, Generative Models

Discipline: Artificial Intelligence , Human-Computer Interaction (HCI)