From Noise to Control: Parameterized Diffusion Policies
Renhao Zhang, Haotian Fu, Mingxi Jia, George Konidaris, Yilun Du, Bruno C. da Silva
Abstract
We propose Parameterized Diffusion Policy (PDP), a framework that learns a diffusion policy parameterized in a smooth continuous space. By structuring a latent manifold such that distances between latents' values reflect the semantic similarity of physical trajectories, we transform diffusion from a mechanism of stochastic diversity into a precise tool for behavior steering. Our approach also enables smooth interpolation between known strategies and efficient generalization to novel constraints without the need to update policy weights. We demonstrate that PDP significantly improves adaptation performance on complex multimodal benchmarks in both simulation and real-robot hardware compared to regular diffusion policy, particularly in scenarios requiring the discovery of novel behaviors.
BibTeX
@inproceedings{
zhang2026from,
title={From Noise to Control: Parameterized Diffusion Policies},
author={Renhao Zhang and Haotian Fu and Mingxi Jia and George Konidaris and Yilun Du and Bruno Castro da Silva},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=bjoDtIUSvx}
}