By optimizing diffusion models with physics-aware rewards during training, you can generate robot motions that are both realistic and executable on real hardware without post-hoc corrections.
This paper improves AI-generated humanoid robot motions by using preference optimization to make them physically realistic. Instead of manually tweaking physics penalties, the method integrates a physics controller directly into training, teaching the motion model to generate movements that work well when converted to real robot commands.