From Noise to Control: Parameterized Diffusion Policies
We propose Parameterized Diffusion Policy (PDP), a framework for learning diffusion policies conditioned on low-dimensional, continuous parameters embedded in a learned behavior manifold. By constructing this manifold so that distances between latent representations reflect the semantic similarity between physical trajectories, we transform diffusion from a mechanism for stochastic diversity into a precise and optimizable tool for behavior steering. Our approach enables smooth interpolation between known strategies and efficient adaptation to novel constraints without updating policy weights. We demonstrate that PDP significantly improves adaptation performance on complex multimodal bench...
| Authors | Renhao Zhang, Haotian Fu, Mingxi Jia et al. |
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| Primary category | cs.AI |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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