Learning Automatic Team Coordination in Human-Machine Partnerships

As AI-enabled machines become increasingly prevalent, there is a strong impetus to harness the complementary strengths of humans and machines to enhance productivity and reduce costs in collaborative workspaces such as manufacturing and warehouses [1]. However, efficient team coordination remains challenging due to the heterogeneity of team agents and the dynamic nature of human agents. Existing exact methods often rely on assumptions and mathematical models, which struggle to scale and accurately predict time-varying human performance [2]. While offline Reinforcement Learning (RL) demonstrates potential, it is time-consuming and heavily reliant on training data, often limited in practical factory settings [3]. Therefore, a scalable and data-efficient team coordination method that considers the varying capabilities of heterogeneous agents in collaborative systems is urgently needed to facilitate effective human-machine partnerships.