A new AI model developed by MIT researchers to streamline operations in robotic warehouses uses deep learning to encode information about the warehouse, robots, paths, tasks, and obstacles, predicting the best areas for robot movement efficiently. This approach aims to enhance warehouse operations and can be applied to other complex planning tasks like computer chip design or pipe routing in large buildings.

Getting hundreds of robots to and from their destinations efficiently while keeping them from crashing into each other is no easy task. It is such a complex problem that even the best path-finding algorithms struggle to keep up with the breakneck pace of e-commerce or manufacturing.

New AI model could streamline operations in a robotic warehouse