From Noise to Control: Parameterized Diffusion Policies

2026-05-29

AI Summary

Traditional diffusion policies struggle to precisely control behavior generation and lack effective modeling of semantically similar trajectories. This work proposes Parametric Diffusion Policy (PDP), which constructs a low-dimensional continuous behavioral manifold to transform the diffusion process into a controllable generative mechanism guided by semantic parameters. This approach enables behavior interpolation and zero-shot adaptation without updating policy weights. By integrating diffusion policy learning, behavioral manifold embedding, and semantic-aware trajectory representation, PDP significantly outperforms existing diffusion-based methods in multimodal robotic tasks—both simulated and real—demonstrating superior control precision and adaptability, particularly in synthesizing novel behaviors.

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. 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 benchmarks in both simulated and real-robot experiments compared to standard diffusion policies, particularly in scenarios requiring the synthesis of novel behaviors.

Problem

• diffusion policies
• behavior steering
• parameterized control
• multimodal adaptation
• trajectory synthesis

Innovation

• Parameterized Diffusion Policy
• behavior manifold
• trajectory synthesis
• zero-shot adaptation
• diffusion-based control