Vision-Language-Action Models: Concepts, Progress, Applications and
  Challenges
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
681e0020bb3bac4b88e07a34
Ranjan Sapkota
claimed_verified
2025-05-09T13:54:15.281Z
67ddd80896ac367438d400a6
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/C1NY6Nv5i0erwLnzCrTUM.png
Ranjan Sapkota
user
RanjanSapkota
681e0020bb3bac4b88e07a35
Yang Cao
681e0020bb3bac4b88e07a36
Konstantinos I. Roumeliotis
claimed_verified
2025-07-17T08:20:17.882Z
64bcce94f8f28a19b0ba6c86
/avatars/45316b6fd19886eb18ff669f08bdd311.svg
Dr. Konstantinos I. Roumeliotis
user
rkonstadinos
681e0020bb3bac4b88e07a37
Manoj Karkee
Vision-Language-Action Models: Concepts, Progress, Applications and
  Challenges
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
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.
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