A Survey of Domain Adaptation in Robotics Using Diffusion Models

Xinyu Huang

doi:10.54254/2755-2721/2025.LD26106

Applied and Computational EngineeringOpen access

A Survey of Domain Adaptation in Robotics Using Diffusion Models

Research Article

Open Access

A Survey of Domain Adaptation in Robotics Using Diffusion Models

Xinyu Huang ^1*

¹ Faculty of Engineering, University of Sydney, Sydney, Australia, NSW 2008

^*Corresponding author: xniyuhimself@gmail.com

Published on 20 August 2025

ACE Vol.179

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-184-6

ISBN (Online): 978-1-80590-129-7

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Abstract

The successful deployment of intelligent robotic systems in the real world is often hampered by the “sim-to-real” gap, the discrepancy between simulated training environments and the complexities of reality. This gap arises from imperfect modeling of physics, rendering artifacts, and sensor noise, leading to policies trained in simulation failing to generalize. Domain adaptation techniques aim to bridge this gap, and recently, diffusion models have emerged as a powerful new paradigm for this task. This survey provides a comprehensive overview and contextual analysis of the application of diffusion models for domain adaptation in robotics. The paper begins by introducing the fundamental concepts of the sim-to-real gap and tracing the evolution of adaptation techniques, from domain randomization and adversarial methods to the current state of the art. The paper then presents a literature survey of recent works, categorizing them by their application in key robotics domains. Following this, a focused and in-depth case study provides a detailed walk-through of specific, influential methods, situating them within the landscape of prior work to highlight their core innovations. This survey then delves into a multifaceted discussion of the current challenges and open problems, including the critical trade-offs between computational efficiency and real-time performance, the debate surrounding generalization versus memorization, and the paramount issues of safety and reliability. The survey concludes by summarizing the state of the art and offering a perspective on the future directions of this rapidly evolving field, which is fundamentally reshaping how the industry approaches robust robotic learning.

Keywords:

Domain Adaption, Diffusion Model, Generative Adversarial Networks (GANs)

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