Techniques for semantically aligned generative augmentation for training policy models

One or more non-transitory computer-readable media storing instructions that, when executed by at least one processor, cause the at least one processor to perform the steps of:

• processing one or more input images using a trained image generative model to generate one or more augmented images, wherein the trained image generative model generates each augmented image included in the one or more augmented images conditioned on an input image included in the one or more input images, depth information associated with the input image, semantic information associated with the input image, and text describing an augmentation to make to the input image;
• and
• performing, based on the one or more augmented images, one or more operations to train an untrained machine learning model to generate a trained machine learning model.