Contractive Diffusion Policies: Robust Action Diffusion via Contractive Score-Based Sampling with Differential Equations
CDPs add a contraction regularizer to diffusion policies that pulls nearby sampling flows together, suppressing solver and score-matching errors and unwanted action variance. Backed by theory and a practical recipe with a single extra hyperparameter, they often outperform standard diffusion policies in simulation and on real robots, most clearly when data is scarce.