视觉-语言-动作模型：概念、进展、应用与挑战

Vision-Language-Action Models: Concepts, Progress, Applications and Challenges

May 7, 2025

作者: Ranjan Sapkota, Yang Cao, Konstantinos I. Roumeliotis, Manoj Karkee

cs.AI

摘要

视觉-语言-动作（VLA）模型标志着人工智能领域的一次革命性进步，旨在将感知、自然语言理解及具身行动统一于单一的计算框架内。本基础性综述全面梳理了VLA模型的最新进展，系统性地围绕构建这一快速演变领域的五大主题支柱展开。我们首先确立了VLA系统的概念基础，追溯其从跨模态学习架构到紧密集成视觉-语言模型（VLMs）、动作规划器及分层控制器的通用智能体的演进历程。研究方法采用严格的文献综述框架，涵盖了过去三年内发表的80余个VLA模型。关键进展领域包括架构创新、参数高效训练策略及实时推理加速。我们探讨了人形机器人、自动驾驶汽车、医疗与工业机器人、精准农业及增强现实导航等多样化应用场景。综述进一步剖析了实时控制、多模态动作表示、系统可扩展性、对未见任务的泛化能力及伦理部署风险等主要挑战。基于最前沿研究，我们提出了针对性解决方案，如智能体AI适应、跨具身泛化及统一的神经符号规划。在展望性讨论中，我们勾勒了未来路线图，其中VLA模型、VLMs与智能体AI将汇聚，共同驱动社会对齐、自适应且通用的具身智能体。本工作为推进智能现实世界机器人及人工通用智能的发展提供了基础性参考。>视觉-语言-动作，智能体AI，AI智能体，视觉-语言模型

English

Vision-Language-Action (VLA) models mark a transformative advancement in artificial intelligence, aiming to unify perception, natural language understanding, and embodied action within a single computational framework. This foundational review presents a comprehensive synthesis of recent advancements in Vision-Language-Action models, systematically organized across five thematic pillars that structure the landscape of this rapidly evolving field. We begin by establishing the conceptual foundations of VLA systems, tracing their evolution from cross-modal learning architectures to generalist agents that tightly integrate vision-language models (VLMs), action planners, and hierarchical controllers. Our methodology adopts a rigorous literature review framework, covering over 80 VLA models published in the past three years. Key progress areas include architectural innovations, parameter-efficient training strategies, and real-time inference accelerations. We explore diverse application domains such as humanoid robotics, autonomous vehicles, medical and industrial robotics, precision agriculture, and augmented reality navigation. The review further addresses major challenges across real-time control, multimodal action representation, system scalability, generalization to unseen tasks, and ethical deployment risks. Drawing from the state-of-the-art, we propose targeted solutions including agentic AI adaptation, cross-embodiment generalization, and unified neuro-symbolic planning. In our forward-looking discussion, we outline a future roadmap where VLA models, VLMs, and agentic AI converge to power socially aligned, adaptive, and general-purpose embodied agents. This work serves as a foundational reference for advancing intelligent, real-world robotics and artificial general intelligence. >Vision-language-action, Agentic AI, AI Agents, Vision-language Models