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This paper aims to enable multi-agent systems to effectively utilize pastmemories to adapt to novel collaborative tasks in a data-efficient fashion. Wepropose the Multi-Agent Coordination Skill Database, a repository for storing acollection of coordinated behaviors associated with key vectors distinctive tothem. Our Transformer-based skill encoder effectively captures spatio-temporalinteractions that contribute to coordination and provides a unique skillrepresentation for each coordinated behavior. By leveraging a small number ofdemonstrations of the target task, the database enables us to train the policyusing a dataset augmented with the retrieved demonstrations. Experimentalevaluations demonstrate that our method achieves a significantly higher successrate in push manipulation tasks compared with baseline methods like few-shotimitation learning. Furthermore, we validate the effectiveness of ourretrieve-and-learn framework in a real environment using a team of wheeledrobots.

Multi-Agent Behavior Retrieval: Retrieval-Augmented Policy Training for Cooperative Manipulation by Mobile Robots