Diffusion Models for Robotic Manipulation: A Survey

2025-04-11

AI Summary

This work addresses three core challenges in robotic manipulation: difficulty in modeling multimodal action distributions, poor robustness to high-dimensional input-output spaces, and scarcity of real-world demonstration data. To this end, we systematically investigate diffusion models for grasp learning, trajectory planning, and data augmentation. We propose a vision-action joint modeling framework that integrates denoising diffusion probabilistic models (DDPMs) with score distillation sampling (SDS), augmented by conditional generation and simulation-to-real domain-cooperative enhancement. We introduce the first taxonomy and evaluation benchmark specifically designed for diffusion models in robotic manipulation. Empirical results demonstrate significant improvements over conventional imitation learning and reinforcement learning paradigms in high-dimensional robustness, few-shot generalization, and cross-modal alignment. The study further identifies scalability, real-time inference efficiency, and physical consistency as three critical directions for future advancement.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Review diffusion models' role in robotic manipulation tasks
• Explore diffusion models for grasp learning and trajectory planning
• Address data scarcity via diffusion-based scene augmentation

Innovation

• Diffusion models enhance robotic manipulation tasks
• Integrate with imitation and reinforcement learning
• Address data scarcity via scene augmentation