This paper aims to enable multi-agent systems to effectively utilize past memories to adapt to novel collaborative tasks in a data-efficient fashion. We propose the Multi-Agent Coordination Skill Database, a repository for storing a collection of coordinated behaviors associated with the key vector distinctive to them. Our Transformer-based skill encoder effectively captures spatio-temporal interactions that contribute to coordination and provide a skill representation unique to each coordinated behavior. By leveraging a small number of demonstrations of the target task, the database allows us to train the policy using a dataset augmented with the retrieved demonstrations. Experimental evaluations clearly demonstrate that our method achieves a significantly higher success rate in push manipulation tasks compared to baseline methods like few-shot imitation learning. Furthermore, we validate the effectiveness of our retrieve-and-learn framework in a real environment using a team of wheeled robots.

翻译：本文旨在使多智能体系统能够高效利用过往记忆，以数据高效的方式适应新颖的协作任务。我们提出多智能体协作技能数据库，这是一个存储与关键向量（独特标识符）相关联的协调行为集合的仓库。基于Transformer的技能编码器能有效捕捉促成协调的时空交互，并为每种协调行为提供独特的技能表征。通过利用目标任务的小样本演示，该数据库使我们能够使用经检索演示增强的数据集来训练策略。实验评估清晰表明，与少量样本模仿学习等基线方法相比，我们的方法在推操控任务中取得了显著更高的成功率。此外，我们通过一组轮式机器人在真实环境中验证了检索-学习框架的有效性。