AINeutralarXiv – CS AI · 6h ago6/10
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From Noise to Control: Parameterized Diffusion Policies
Researchers propose Parameterized Diffusion Policy (PDP), a machine learning framework that enables diffusion models to learn controllable behaviors through low-dimensional parameters mapped to a semantic behavior manifold. This approach transforms diffusion models from stochastic noise generators into precise policy control tools, allowing smooth interpolation between strategies and adaptation to novel constraints without retraining.