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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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A Survey on Vision-Language-Action Models for Embodied AI.

Yueen Ma, Zixing Song, Yuzheng Zhuang

IEEE Transactions on Neural Networks and Learning Systems

|April 28, 2026

Summary

This summary is machine-generated.

This survey introduces vision-language-action (VLA) models, crucial for embodied artificial intelligence (AI) and artificial general intelligence (AGI). It categorizes VLA research and resources, outlining future directions for AI agents in the physical world.

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Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

Embodied AI is key to artificial general intelligence (AGI), requiring control of physical agents.
Large language models (LLMs) and vision-language models (VLMs) have advanced AI capabilities.
Vision-language-action (VLA) models bridge language understanding with physical action generation for embodied AI tasks.

Purpose of the Study:

To provide the first comprehensive survey of vision-language-action (VLA) models in embodied AI.
To establish a taxonomy for the rapidly evolving field of VLA models.
To consolidate resources and identify future research trajectories.

Main Methods:

Categorization of VLA research into three main areas: individual components, VLA-based control policies for low-level actions, and high-level task planners.
Compilation of datasets, simulators, and benchmarks relevant to VLA research.
Analysis of current challenges and future directions in embodied AI.

Main Results:

A structured taxonomy detailing the landscape of VLA models.
Identification of VLA applications in predicting actions and decomposing complex tasks.
A comprehensive overview of essential resources for VLA research and development.

Conclusions:

VLAs are a critical advancement for embodied AI, enabling robots to perform complex, language-conditioned tasks.
The survey provides a foundational understanding and roadmap for future research in VLA models and embodied AI.
Continued development in VLA models promises significant progress towards artificial general intelligence.