Pure Vision Language Action (VLA) Models: A Comprehensive Survey
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915608261558272 |
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| author | Zhang, Dapeng Sun, Jing Hu, Chenghui Wu, Xiaoyan Yuan, Zhenlong Zhou, Rui Shen, Fei Zhou, Qingguo |
| author_facet | Zhang, Dapeng Sun, Jing Hu, Chenghui Wu, Xiaoyan Yuan, Zhenlong Zhou, Rui Shen, Fei Zhou, Qingguo |
| contents | The emergence of Vision Language Action (VLA) models marks a paradigm shift from traditional policy-based control to generalized robotics, reframing Vision Language Models (VLMs) from passive sequence generators into active agents for manipulation and decision-making in complex, dynamic environments. This survey delves into advanced VLA methods, aiming to provide a clear taxonomy and a systematic, comprehensive review of existing research. It presents a comprehensive analysis of VLA applications across different scenarios and classifies VLA approaches into several paradigms: autoregression-based, diffusion-based, reinforcement-based, hybrid, and specialized methods; while examining their motivations, core strategies, and implementations in detail. In addition, foundational datasets, benchmarks, and simulation platforms are introduced. Building on the current VLA landscape, the review further proposes perspectives on key challenges and future directions to advance research in VLA models and generalizable robotics. By synthesizing insights from over three hundred recent studies, this survey maps the contours of this rapidly evolving field and highlights the opportunities and challenges that will shape the development of scalable, general-purpose VLA methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19012 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pure Vision Language Action (VLA) Models: A Comprehensive Survey Zhang, Dapeng Sun, Jing Hu, Chenghui Wu, Xiaoyan Yuan, Zhenlong Zhou, Rui Shen, Fei Zhou, Qingguo Robotics Artificial Intelligence The emergence of Vision Language Action (VLA) models marks a paradigm shift from traditional policy-based control to generalized robotics, reframing Vision Language Models (VLMs) from passive sequence generators into active agents for manipulation and decision-making in complex, dynamic environments. This survey delves into advanced VLA methods, aiming to provide a clear taxonomy and a systematic, comprehensive review of existing research. It presents a comprehensive analysis of VLA applications across different scenarios and classifies VLA approaches into several paradigms: autoregression-based, diffusion-based, reinforcement-based, hybrid, and specialized methods; while examining their motivations, core strategies, and implementations in detail. In addition, foundational datasets, benchmarks, and simulation platforms are introduced. Building on the current VLA landscape, the review further proposes perspectives on key challenges and future directions to advance research in VLA models and generalizable robotics. By synthesizing insights from over three hundred recent studies, this survey maps the contours of this rapidly evolving field and highlights the opportunities and challenges that will shape the development of scalable, general-purpose VLA methods. |
| title | Pure Vision Language Action (VLA) Models: A Comprehensive Survey |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.19012 |