Domain Randomization for Sim2Real Transfer
Read OriginalThis technical article discusses the challenge of transferring models trained in simulation to real-world robots, known as the sim2real gap. It compares approaches like system identification and domain adaptation, then focuses on domain randomization (DR). DR involves training models across a wide variety of randomized simulated environments to create a robust policy that can generalize to the physical world, often with little to no real-world data.
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