What is domain randomization?

Domain randomization is a technique in machine learning and robotics that involves artificially increasing the diversity of data during model training by randomly changing the parameters of the simulation environment. The goal is to make the model more robust to variations in the real world and improve its generalization ability.

For example, when training a robot in a simulator, lighting, textures, object positions, sensor noises, and other parameters can be randomly changed. As a result, the model does not overfit to specific simulator conditions and transfers better to real-world tasks, where conditions can differ significantly from training.

This is especially useful in robotics, where collecting large amounts of real data is costly and labor-intensive, and simulation with domain randomization helps prepare the robot for the real world.