Citation :

Akash Vijayrao Chaudhari, Pallavi Ashokrao Charate, 2025. "Integrating Diffusion Models into Model-Based Reinforcement Learning for Real-Time Robotic Control A Theoretical Review" ESP International Journal of Science, Humanities & Management Studies(ESP-IJSHMS)  Volume 3, Issue 1: 10-18.

Abstract :

Diffusion models – a class of generative deep learning models based on iterative denoising – have emerged as powerful tools in machine learning, especially in image and sequence generation. Concurrently, model-based reinforcement learning (MBRL) has shown promise in enabling robots to plan and adapt their behavior using internal models of the environment. This review provides a comprehensive theoretical overview of recent research that integrates diffusion models into MBRL for real-time robotic control. We first summarize the foundations of diffusion models and MBRL, highlighting how diffusion’s ability to model complex, multi-modal distributionsar5iv.org and MBRL’s use of internal environment modelslink.springer.com can complement each other. We then survey existing methods that combine these techniques: from diffusion-based trajectory planners that treat planning as an iterative denoising processarxiv.orgdiffusion-planning.github.io, to diffusion policies that serve as powerful parametric policies in offline RL settingshuggingface.codiffusion-policy.cs.columbia.edu. The integration frameworks, their theoretical underpinnings, and key design considerations are discussed in depth. We also review use cases in robotic manipulation, locomotion, and multi-robot systems, examining how diffusion-integrated MBRL addresses real-time control challenges. Advantages of this integration – such as handling multi-modal uncertaintyarxiv.org and improving training stabilitydiffusion-policy.cs.columbia.edu – are contrasted with challenges like computational efficiency and real-world adaptation. Recent advancements (e.g., efficient diffusion sampling for faster controlarxiv.org) are highlighted, and a comparative analysis of state-of-the-art methods is presented in tabular form. Finally, we outline future directions, including opportunities to improve real-time performance, ensure safety, and combine diffusion models with other emerging paradigms. This review is intended to serve as a consolidated reference for researchers and practitioners interested in the theoretical foundations and state-of-the-art developments at the intersection of diffusion modeling and reinforcement learning in robotics.

References :

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Keywords :

Diffusion Models, Model-Based Reinforcement Learning (MBRL), Robotic Control, Generative Modeling, Real-Time Planning, Multi-Modal Uncertainty, Trajectory Optimization, Diffusion Policies, Offline Reinforcement Learning, Robotics.