Systematic Literature Review of Vision-Language-Action Models for Generalist Robots

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Generalist robots need to perform diverse tasks while operating in dynamic, uncertain, and unstructured environments, often around human beings. Vision-language-action (VLA) models have recently emerged as a promising and flexible framework for integrating perception, reasoning, robotic control, and action execution to develop generalist robotic policies. This systematic literature review (SLR) examines more than 140 VLA-related publications between 2020 and 2025 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. To the best of our knowledge, it is the first PRISMA-compliant systematic review dedicated to VLA models, offering a structured discussion of robotic policies, VLA architectures, and inference optimization methods. The review also presents descriptive analyses of the included studies and a glossary defining the terminology commonly used in VLA and generalist robotic policy research. The findings reveal substantial diversity among VLA models in terms of their supported modalities, robotic embodiments, training strategies, and architectural designs. Despite the rapid growth of VLA research, several important areas remain underexplored, including the execution of complex, long-horizon tasks, effective integration of speech, and deployment on low-cost hardware, while ensuring robust, safe, and secure operation.