Domain Randomization Theories

Domain randomization is a powerful technique in the field of computer vision and robotics that is used for data generation and training machine learning models. It involves deliberately introducing variations and diversity into the training data by randomizing various aspects of the environment, such as lighting conditions, textures, camera angles, and object poses. The goal of domain randomization is to create a synthetic dataset that is sufficiently diverse and representative of real-world scenarios, enabling machine learning models to become more robust and adaptable to a wide range of environmental conditions. By training on these randomized datasets, models can learn to generalize better and perform effectively in the face of unpredictability and variability, making them more capable of real-world applications where environmental factors are often challenging to control or predict.