Zero-shot human-AI coordination is the training of an ego-agent to coordinate with humans without using human data. Most studies on zero-shot human-AI coordination have focused on enhancing the ego-agent's coordination ability in a given environment without considering the issue of generalization to unseen environments. Real-world applications of zero-shot human-AI coordination should consider unpredictable environmental changes and the varying coordination ability of co-players depending on the environment. Previously, the multi-agent UED (Unsupervised Environment Design) approach has investigated these challenges by jointly considering environmental changes and co-player policy in competitive two-player AI-AI scenarios. In this paper, our study extends the multi-agent UED approach to a zero-shot human-AI coordination. We propose a utility function and co-player sampling for a zero-shot human-AI coordination setting that helps train the ego-agent to coordinate with humans more effectively than the previous multi-agent UED approach. The zero-shot human-AI coordination performance was evaluated in the Overcooked-AI environment, using human proxy agents and real humans. Our method outperforms other baseline models and achieves a high human-AI coordination performance in unseen environments.

本研究解决了现有零样本人机协调方法在未见环境中的泛化能力问题。通过扩展多智能体无监督环境设计方法，提出了一种新的效用函数和合作玩家采样策略，从而提升了自我代理与人类的协调能力。实验结果表明，该方法在未知环境中显著提高了人机协调性能，优于其他基线模型。

零样本人机协调的自动课程设计