Closed-Loop Action Chunks with Dynamic Corrections for Training-Free Diffusion Policy

2026-03-02

AI Summary

This work addresses the limited responsiveness and poor adaptability of diffusion-based policies in dynamic environments, which often lead to task failure. To overcome these limitations, the authors propose the DCDP framework, which integrates chunked action generation with a training-free, real-time closed-loop correction mechanism to significantly enhance robotic responsiveness and adaptability in dynamic scenarios. The approach innovatively combines a self-supervised dynamic feature encoder, cross-attention fusion, and an asymmetric action encoding-decoding architecture to enable efficient online adjustment. Evaluated on the dynamic PushT simulation benchmark, DCDP improves task adaptability by 19% with only a 5% increase in computational overhead and supports plug-and-play deployment on real-world robotic manipulation tasks.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Diffusion Models for Vision

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Diffusion-based policies have achieved remarkable results in robotic manipulation but often struggle to adapt rapidly in dynamic scenarios, leading to delayed responses or task failures. We present DCDP, a Dynamic Closed-Loop Diffusion Policy framework that integrates chunk-based action generation with real-time correction. DCDP integrates a self-supervised dynamic feature encoder, cross-attention fusion, and an asymmetric action encoder-decoder to inject environmental dynamics before action execution, achieving real-time closed-loop action correction and enhancing the system's adaptability in dynamic scenarios. In dynamic PushT simulations, DCDP improves adaptability by 19\% without retraining while requiring only 5\% additional computation. Its modular design enables plug-and-play integration, achieving both temporal coherence and real-time responsiveness in dynamic robotic scenarios, including real-world manipulation tasks. The project page is at: https://github.com/wupengyuan/dcdp

Problem

• diffusion policy
• dynamic adaptation
• robotic manipulation
• closed-loop control
• real-time response

Innovation

• Diffusion Policy
• Closed-Loop Control
• Dynamic Correction
• Chunk-Based Action Generation
• Self-Supervised Encoding