Machine learning system fails when the training data distribution is significantly different from testing data. For a “simple” problem like image classification, we can avoid this problem by including sufficient diversity for the training data. But for more complicated real-world problems, such as robotic AI, there are simply too many different possibilities that one cannot…
Domain randomization
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