Responsive Noise-Relaying Diffusion Policy: Responsive and Efficient Visuomotor Control

TL;DR

The paper introduces RNR-DP, a diffusion-based policy that improves robot responsiveness and efficiency by generating immediate actions conditioned on recent observations, outperforming previous diffusion methods in success rate and speed.

Contribution

It proposes a novel noise-relaying diffusion policy with sequential denoising for responsive, real-time robot control, addressing limitations of prior diffusion approaches.

Findings

Achieves 18% higher success rate on response-sensitive tasks.

Outperforms DDIM by 6.9% in success rate on regular tasks.

Enables faster action generation while maintaining motion consistency.

Abstract

Imitation learning is an efficient method for teaching robots a variety of tasks. Diffusion Policy, which uses a conditional denoising diffusion process to generate actions, has demonstrated superior performance, particularly in learning from multi-modal demonstrates. However, it relies on executing multiple actions predicted from the same inference step to retain performance and prevent mode bouncing, which limits its responsiveness, as actions are not conditioned on the most recent observations. To address this, we introduce Responsive Noise-Relaying Diffusion Policy (RNR-DP), which maintains a noise-relaying buffer with progressively increasing noise levels and employs a sequential denoising mechanism that generates immediate, noise-free actions at the head of the sequence, while appending noisy actions at the tail. This ensures that actions are responsive and conditioned on the latest observations, while maintaining motion consistency through the noise-relaying buffer. This design enables the handling of tasks requiring responsive control, and accelerates action generation by reusing denoising steps. Experiments on response-sensitive tasks demonstrate that, compared to Diffusion Policy, ours achieves 18% improvement in success rate. Further evaluation on regular tasks demonstrates that RNR-DP also exceeds the best acceleration method (DDIM) by 6.9% in success rate, highlighting its computational efficiency advantage in scenarios where responsiveness is less critical. Our project page is available at https://rnr-dp.github.io