视觉−语言−动作VLA大模型驱动的平行艺术创作架构

鲁越; 窦勇; 倪清桦; 郭超; 白天翔

doi:10.16383/j.aas.c250565

视觉−语言−动作VLA大模型驱动的平行艺术创作架构

doi: 10.16383/j.aas.c250565 cstr: 32138.14.j.aas.c250565

鲁越^1,,
窦勇^2,,
倪清桦^2,,
郭超^3,,
白天翔^4,

1.
沈阳工业大学信息科学与工程学院沈阳 110870
2.
澳门科技大学创新工程学院工程科学系澳门 999078
3.
中国科学院自动化研究所北京 100190
4.
航天科工集团智能科技研究院有限公司北京 100043

基金项目: 国家自然科学基金(62503280, 62306319), DeSci国际组织项目(DeSCII-0001-0123-2021)资助

详细信息

作者简介:
鲁越：沈阳工业大学信息科学与工程学院讲师. 主要研究方向为人工智能艺术, 机器学习, 智能机器人系统. E-mail: luyue@sut.edu.cn

窦勇：澳门科技大学创新工程学院工程科学系博士研究生. 主要研究方向为机器艺术, 智能机器人系统, 机器学习. E-mail: douyong@fortunevc.com

倪清桦：澳门科技大学创新工程学院工程科学系博士研究生. 主要研究方向为平行智能, 决策智能, 区块链, 去中心化自治组织系统, 平行戏剧. E-mail: qinghua.ni@ieee.org

郭超：中国科学院自动化研究所助理研究员. 主要研究方向为人工智能艺术创作, 大模型, 智能体以及智能机器人系统.本文通信作者. E-mail: chao.guo@ia.ac.cn

白天翔：航天科工集团智能科技研究院有限公司工程师. 主要研究方向为强化学习, 复杂系统, 人机智能. E-mail: flyinskybtx@sina.com

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出版历程
- 收稿日期: 2025-10-22
- 录用日期: 2026-02-13
- 网络出版日期: 2026-11-11

Large Vision-Language-Action Model-Driven Parallel Art Creation Framework

LU Yue^1
,,
DOU Yong^2
,,
NI Qing-Hua^2
,,
GUO Chao^3
,,
BAI Tian-Xiang^4
,

1.
School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870
2.
Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macao 999078
3.
Institute of Automation, Chinese Academy of Sciences, Beijing 100190
4.
Aerospace Science and Industry Group Intelligent Technology Research Institute Co., Ltd., Beijing 100043

Funds: Supported by the National Natural Science Foundation of China (62503280, 62306319), and the DeSci International Project (DeSCII-0001-0123-2021)

More Information

Author Bio:
LU Yue　Lecturer at the School of Information Science and Engineering, Shenyang University of Technology. His research interests include AI for art, machine learning, and intelligent robotic systems

DOU Yong　Ph.D. candidate at the Faculty of Innovation Engineering, Macau University of Sciences and Technology. His research interests include robotic art, intelligent robotic systems, and machine learning

NI Qing-Hua　Ph.D. candidate at the Faculty of Innovation Engineering, Macau University of Sciences and Technology. Her research interests include parallel intelligence, decision intelligence, blockchain, decentralized autonomous organization, and parallel theater

Guo Chao　Assistant Professor in the Institute of Automation, Chinese Academy of Sciences. His research interests include AI for creation, large models, agents, and intelligent robotic systems. Corresponding author of this paper

Bai Tian-Xiang　Engineer at the Aerospace Science and Industry Group Intelligent Technology Research Institute Co., Ltd. His research interests include reinforcement learning, complex systems, and human-machine intelligence

摘要

摘要: 近年来, 大模型等技术的迅速发展推动了人工智能与艺术创作的深度融合, 机器辅助乃至自主创作系统相继涌现, 在人类艺术创作中发挥日益重要的作用. 然而, 现有的主流艺术创作方法多停留在创作结果直接生成, 与人类艺术家逐步塑造的动态创作过程存在显著差距, 制约了机器艺术创作的进一步发展. 为提升人工智能创作系统对人类创作思维与过程的模拟能力, 本文基于平行系统理论与ACP方法, 提出视觉−语言−动作大模型驱动的平行艺术创作架构. 该架构依托平行系统的描述学习、预测学习与引导学习, 进行艺术创作全流程建模, 构建具备跨模态感知、语义理解与具身执行能力的创作智能体, 为人工智能创作系统的类人化演进提供新范式, 推动机器艺术创作由单一结果生成迈向创作过程自主完成. 文中阐述所提方法的总体结构、核心组成与主要特征, 并结合典型案例进行分析讨论, 最后总结全文并展望平行艺术的未来发展方向.
- 人工智能艺术创作 /
- 视觉−语言−动作大模型 /
- 平行艺术 /
- 平行系统
Abstract: In recent years, the rapid advancement of large models has driven a deep integration between artificial intelligence (AI) and artistic creation. Machine-assisted and even autonomous creation systems have emerged successively, playing an increasingly significant role in human artistic production. However, most existing approaches to artistic creation remain focused on directly producing visual outcomes, exhibiting a notable gap from the dynamic creative processes of human artists characterized by progressive construction. This limitation constrains the further development of machine-based artistic creation. To improve the capability of AI creation systems in modeling human creative thinking and processes, this paper proposes a parallel artistic creation system driven by large vision-language-action (VLA) models, grounded in parallel systems theory and the ACP (artificial systems, computational experiments, and parallel execution) approach. The proposed framework employs the Descriptive Learning, Predictive Learning, and Prescriptive Learning of parallel systems to achieve full-process modeling of artistic creation, thereby constructing a creative agent with integrated cross-modal perception, semantic understanding, and embodied execution capabilities. This framework provides a new paradigm for the human-like evolution of AI art systems, advancing machine artistic creation from result-oriented generation toward autonomous realization of the entire creative process. The paper elaborates on the overall architecture, core components, and primary features of the proposed system, presents analyses through representative case studies, and concludes with a summary and outlook on the future development of parallel art.
- AI artistic creation /
- large vision-language-action models /
- parallel art /
- parallel systems

HTML全文

图 1 VLA大模型驱动的平行艺术创作系统

Fig. 1 The large VLA model-driven parallel art creation system

下载: 全尺寸图片幻灯片

图 2 VLA大模型驱动的平行艺术创作系统描述智能

Fig. 2 Descriptive Intelligence in the large VLA model-driven parallel art creation system

下载: 全尺寸图片幻灯片

图 3 VLA大模型驱动的平行艺术创作系统预测学习

Fig. 3 Predictive Learning in the large VLA model-driven parallel art creation system

下载: 全尺寸图片幻灯片

图 4 VLA大模型驱动的平行艺术创作系统引导学习

Fig. 4 Prescriptive Learning in the large VLA model-driven parallel art creation system

下载: 全尺寸图片幻灯片

图 5 平行艺术创作机器人系统典型案例

Fig. 5 Typical case of the parallel art creation robotic system

下载: 全尺寸图片幻灯片

图 6 平行艺术创作机器人系统作品样例

Fig. 6 Examples of artworks created by the parallel art creation robotic system

下载: 全尺寸图片幻灯片

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