MIT and Symbotic researchers developed a hybrid AI system that uses deep reinforcement learning combined with classical planning algorithms to coordinate hundreds of robots in automated warehouses. The system learns which robots to prioritize at each moment to prevent congestion and collisions, adapting dynamically to changing warehouse conditions. In simulations based on real e-commerce warehouse layouts, the approach achieved roughly 25% greater throughput compared to traditional expert-designed algorithms, with the gains becoming more pronounced as robot density increases. The system also generalizes to new warehouse layouts without retraining from scratch.
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