Vision-Language-Action (VLA) models are driving a revolution in robotics, enabling machines to understand instructions and interact with the physical world. This field is exploding with new models and datasets, making it both exciting and challenging to keep pace with. This survey offers a clear and structured guide to the VLA landscape. We design it to follow the natural learning path of a researcher: we start with the basic Modules of any VLA model, trace the history through key Milestones, and then dive deep into the core Challenges that define recent research frontier. Our main contribution is a detailed breakdown of the five biggest challenges in: (1) Representation, (2) Execution, (3) Generalization, (4) Safety, and (5) Dataset and Evaluation. This structure mirrors the developmental roadmap of a generalist agent: establishing the fundamental perception-action loop, scaling capabilities across diverse embodiments and environments, and finally ensuring trustworthy deployment-all supported by the essential data infrastructure. For each of them, we review existing approaches and highlight future opportunities. We position this paper as both a foundational guide for newcomers and a strategic roadmap for experienced researchers, with the dual aim of accelerating learning and inspiring new ideas in embodied intelligence. A live version of this survey, with continuous updates, is maintained on our \href{https://suyuz1.github.io/VLA-Survey-Anatomy/}{project page}.
@article{arxiv.2512.11362,
title = {An Anatomy of Vision-Language-Action Models: From Modules to Milestones and Challenges},
author = {Chao Xu and Suyu Zhang and Yang Liu and Baigui Sun and Weihong Chen and Bo Xu and Qi Liu and Juncheng Wang and Shujun Wang and Shan Luo and Jan Peters and Athanasios V. Vasilakos and Stefanos Zafeiriou and Jiankang Deng},
journal= {arXiv preprint arXiv:2512.11362},
year = {2025}
}