Diffusion Model

A generative model that learns to reverse a gradual noising process: training adds Gaussian noise to data over T steps, and the model learns to denoise at each step. Sampling generates data by starting from pure noise and iteratively denoising. Diffusion models produce high-quality, diverse samples and have been adapted for robot action generation as Diffusion Policies.