title: Vision-Language-Action Models: Concepts, Progress, Applications and
    Challenges
summary: 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
paper_authors._id: 681e0020bb3bac4b88e07a34
paper_authors.name: Ranjan Sapkota
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-09T13:54:15.281Z
paper_authors._id: 67ddd80896ac367438d400a6
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/C1NY6Nv5i0erwLnzCrTUM.png
paper_authors.fullname: Ranjan Sapkota
paper_authors.type: user
paper_authors.user: RanjanSapkota
paper_authors._id: 681e0020bb3bac4b88e07a35
paper_authors.name: Yang Cao
paper_authors._id: 681e0020bb3bac4b88e07a36
paper_authors.name: Konstantinos I. Roumeliotis
paper_authors._id: 681e0020bb3bac4b88e07a37
paper_authors.name: Manoj Karkee
paper_title: Vision-Language-Action Models: Concepts, Progress, Applications and
    Challenges
paper_summary: 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
paper_ai_summary: A comprehensive review presents advancements in Vision-Language-Action models, covering innovations, training strategies, and real-time applications across various domains, while addressing challenges and proposing future solutions.
paper_ai_keywords: Vision-Language-Action Agentic AI AI Agents Vision-Language Models cross-modal learning hierarchical controllers real-time control multimodal action representation system scalability generalization ethical deployment neuro-symbolic planning