A Survey on Efficient Vision-Language-Action Models
Vision-Language-Action models (VLAs) represent a significant frontier in
embodied intelligence, aiming to bridge digital knowledge with physical-world
interaction. While these models have demonstrated remarkable generalist
capabilities, their deployment is severely hampered by the substantial
computational and data requirements inherent to their underlying large-scale
foundation models. Motivated by the urgent need to address these challenges,
this survey presents the first comprehensive review of Efficient
Vision-Language-Action models (Efficient VLAs) across the entire
data-model-training process. Specifically, we introduce a unified taxonomy to
systematically organize the disparate efforts in this domain, categorizing
current techniques into three core pillars: (1) Efficient Model Design,
focusing on efficient architectures and model compression; (2) Efficient
Training, which reduces computational burdens during model learning; and (3)
Efficient Data Collection, which addresses the bottlenecks in acquiring and
utilizing robotic data. Through a critical review of state-of-the-art methods
within this framework, this survey not only establishes a foundational
reference for the community but also summarizes representative applications,
delineates key challenges, and charts a roadmap for future research. We
maintain a continuously updated project page to track our latest developments:
https://evla-survey.github.io/
6908bb5b812eca10f9cc6066
Zhaoshu Yu
6908bb5b812eca10f9cc6067
Bo Wang
6908bb5b812eca10f9cc6068
Pengpeng Zeng
6908bb5b812eca10f9cc6069
Haonan Zhang
6908bb5b812eca10f9cc606a
Ji Zhang
6908bb5b812eca10f9cc606b
Lianli Gao
6908bb5b812eca10f9cc606c
Jingkuan Song
6908bb5b812eca10f9cc606d
Nicu Sebe
6908bb5b812eca10f9cc606e
Heng Tao Shen
A Survey on Efficient Vision-Language-Action Models
Vision-Language-Action models (VLAs) represent a significant frontier in
embodied intelligence, aiming to bridge digital knowledge with physical-world
interaction. While these models have demonstrated remarkable generalist
capabilities, their deployment is severely hampered by the substantial
computational and data requirements inherent to their underlying large-scale
foundation models. Motivated by the urgent need to address these challenges,
this survey presents the first comprehensive review of Efficient
Vision-Language-Action models (Efficient VLAs) across the entire
data-model-training process. Specifically, we introduce a unified taxonomy to
systematically organize the disparate efforts in this domain, categorizing
current techniques into three core pillars: (1) Efficient Model Design,
focusing on efficient architectures and model compression; (2) Efficient
Training, which reduces computational burdens during model learning; and (3)
Efficient Data Collection, which addresses the bottlenecks in acquiring and
utilizing robotic data. Through a critical review of state-of-the-art methods
within this framework, this survey not only establishes a foundational
reference for the community but also summarizes representative applications,
delineates key challenges, and charts a roadmap for future research. We
maintain a continuously updated project page to track our latest developments:
https://evla-survey.github.io/
This survey reviews Efficient Vision-Language-Action models, addressing computational and data challenges through model design, training, and data collection techniques.
Efficient Vision-Language-Action models Efficient VLAs efficient architectures model compression efficient training efficient data collection embodied intelligence digital knowledge physical-world interaction large-scale foundation models