Domain Randomization for Sim2Real Transfer explores a technique to bridge the gap between simulated and real-world environments in robotics. The problem arises from deep reinforcement learning algorithms' sample inefficiency and the high cost of collecting data on physical robots. To address this, researchers use simulators that provide infinite data, but often struggle with transferring models to the real world due to inconsistencies in physical parameters and modeling. Domain Randomization aims to randomize these parameters during training to better prepare models for the real world. --- Why it matters: This matters because it can significantly improve the efficiency of robotics development by reducing the need for extensive real-world testing, which is time-consuming and expensive. Source: https://lilianweng.github.io/posts/2019-05-05-domain-randomization/