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 e-commerce warehouses. The system learns which robots to prioritize at each moment to prevent congestion, achieving roughly 25% greater throughput than traditional expert-designed algorithms in simulations. The approach adapts to different warehouse layouts and robot densities, and scales better than conventional methods as robot density increases. Real-world deployment is still a future goal, with plans to extend the system to task assignment and larger fleets.
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