From Noise to Control: Parameterized Diffusion Policies

📰 ArXiv cs.AI

Learn to control diffusion policies using parameterized techniques for precise behavior steering

advanced Published 2 Jun 2026

Action Steps

Construct a behavior manifold to embed low-dimensional parameters
Condition diffusion policies on the learned manifold to enable precise control
Optimize the diffusion policy using the semantic similarity between physical trajectories
Implement the Parameterized Diffusion Policy (PDP) framework in a reinforcement learning environment
Evaluate the performance of the PDP framework using metrics such as precision and recall

Who Needs to Know This

Researchers and engineers working on reinforcement learning and control systems can benefit from this framework to improve the precision of their models

Key Insight

💡 Diffusion policies can be transformed from a stochastic mechanism to a precise tool for behavior steering using parameterized techniques

Key Takeaways

Learn to control diffusion policies using parameterized techniques for precise behavior steering

Full Article

Title: From Noise to Control: Parameterized Diffusion Policies

Abstract:
arXiv:2606.00336v1 Announce Type: new Abstract: 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

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