  Deep learning has demonstrated remarkable success across many domains,
including computer vision, natural language processing, and reinforcement
learning. Representative artificial neural networks in these fields span
convolutional neural networks, Transformers, and deep Q-networks. Built upon
unimodal neural networks, numerous multi-modal models have been introduced to
address a range of tasks such as visual question answering, image captioning,
and speech recognition. The rise of instruction-following robotic policies in
embodied AI has spurred the development of a novel category of multi-modal
models known as vision-language-action models (VLAs). Their multi-modality
capability has become a foundational element in robot learning. Various methods
have been proposed to enhance traits such as versatility, dexterity, and
generalizability. Some models focus on refining specific components through
pretraining. Others aim to develop control policies adept at predicting
low-level actions. Certain VLAs serve as high-level task planners capable of
decomposing long-horizon tasks into executable subtasks. Over the past few
years, a myriad of VLAs have emerged, reflecting the rapid advancement of
embodied AI. Therefore, it is imperative to capture the evolving landscape
through a comprehensive survey.
