Domain Randomization

Domain randomization is a technique used in the training of machine learning models, particularly in reinforcement learning and robotics, where the parameters of the simulated environment are varied randomly during training. This approach aims to improve the robustness and generalization of the trained models when deployed in real-world scenarios. Mathematically, domain randomization can be viewed as a form of data augmentation, where the distribution of training data is expanded by introducing variability in the simulation parameters, such as lighting, textures, and object dynamics. Key algorithms include those for adaptive sampling and policy optimization, which help agents learn to perform tasks across a wide range of conditions. This technique is particularly relevant in addressing the sim-to-real gap, as it prepares models to handle the uncertainties and variations present in real environments.