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Researchers from MIT and Stanford University have developed a new machine-learning approach that could be used to control robots, such as drones and autonomous vehicles, more effectively and efficiently in dynamic environments. This technique incorporates structure from control theory into the process for learning a model, which leads to an effective method of controlling complex dynamics. This approach is able to learn an effective controller using fewer data than other approaches, which could help the system achieve better performance faster in real-world scenarios.
