Diffusion models in robotics: a comprehensive review
Diffusion models in robotics: a comprehensive review
This paper presents a comprehensive review of diffusion models in robotics, focusing on their application in robot learning, data scaling, reinforcement learning, and imitation learning. The authors conducted a systematic literature review, selecting peer-reviewed articles and high-impact preprints to analyze the current state of diffusion model research in robotics. The paper discusses various applications of diffusion models, including semantic-level data augmentation, cross-view and morphology synthesis, automated simulation asset and task generation, trajectory optimization, policy representation, hierarchical planning, and real-time deployment. It also addresses the challenges and limitations of current approaches, such as computational cost, inference latency, and data efficiency, while highlighting promising future directions for research and development in the field.
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This paper provides a comprehensive review of diffusion models in robotics, covering their application in robot learning, data scaling, reinforcement learning, and imitation learning. It highlights the challenges and advancements in integrating diffusion models with real-world robotic tasks, emphasizing the importance of semantic-level data augmentation, cross-view synthesis, and automated data-ga
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Source：Robotics — Paper and dataset web discovery · frontiersin.org