This Columbia University project presents Diffusion Policy, a method for learning robotic visuomotor policies through action diffusion models. It provides state-based and vision-based implementations for robot control, with Colab notebooks and full experiment logs including checkpoints and training configurations. The approach applies generative diffusion models to robot action prediction, enabling imitation learning for manipulation tasks.