Due to its factorized architecture, Diffusion Model Predictive Control (DMPC) can adapt to changes in an environment's dynamics by re-training only its dynamics model component on new data.

A key advantage of Model Predictive Control (MPC) is its ability to adapt to new reward functions at test time without retraining.

The Diffusion Policy model for robotics requires expert demonstrations to train and operates within a behavior cloning paradigm.