REVIEW article
Front. Robot. AI
Sec. Robot Learning and Evolution
Volume 12 - 2025 | doi: 10.3389/frobt.2025.1606247
Diffusion Models for Robotic Manipulation: A Survey
Provisionally accepted
- Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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Diffusion generative models have demonstrated remarkable success in visual domains such as image and video generation. They have also recently emerged as a promising approach in robotics, especially in robot manipulations. Diffusion models leverage a probabilistic framework, and they stand out with their ability to model multi-modal distributions and their robustness to high-dimensional input and output spaces. This survey provides a comprehensive review of state-of-the-art diffusion models in robotic manipulation, including grasp learning, trajectory planning, and data augmentation. Diffusion models for scene and image augmentation lie at the intersection of robotics and computer vision for vision-based tasks to enhance generalizability and data scarcity. This paper also presents the two main frameworks of diffusion models and their integration with imitation learning and reinforcement learning. In addition, it discusses the common architectures and benchmarks and points out the challenges and advantages of current state-of-the-art diffusion-based methods.
Keywords: diffusion models, robot manipulation learning, Generative models, Imitation learning, Grasp learning
Received: 04 Apr 2025; Accepted: 14 Jul 2025.
Copyright: © 2025 Wolf, Shi, Liu and Rayyes. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Rosa Petra Wolf, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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