Google’s AI learns how to navigate environments from limited data

Carnegie Mellon, Google, and Stanford researchers write in a paper that they’ve developed a framework for using weak supervision a form of AI training where the model learns from large amounts of limited, imprecise, or noisy data that enables robots to efficiently explore a challenging environment. By teaching the robots to reach only the areas of their surroundings that are relevant, the researchers say their approach speeds up training on various robot manipulation tasks.

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