Year: 2025
Type: preprint
Abstract: This survey provides an in-depth analysis of recent advancements in foundational models that leverage large and diverse datasets to enable flexible and general-purpose manipulation skills. We reviewed the main concepts of Vision-Language-Action (VLA) models with emphasis on their connection to broader trends in robot learning and the architectural strategies that facilitate integration across vision, language, and action. The survey contains current training methodologies, dataset construction techniques, and the role of high-quality demonstrations in achieving reliable performance across diverse tasks. Key trends are identified, including the adoption of open-source tools, scaling of model size and training data, and improved generalization across platforms. In addition, we noted that VLA models demonstrate strong performance in controlled environments; however, significant challenges persist in long-term task planning, failure recovery, and operation in complex real-world scenarios. This survey aims to serve as a comprehensive reference for researchers and outline future research directions that may advance the practical deployment of VLA models in robotic manipulation.
Authors: Tomohiro Motoda, Koshi Makihara, Ryoichi Nakajo, Hanbit Oh, Keisuke Shirai, Ryo Hanai, Masaki Murooka, Yuma Suzuki, Hiroki Nishihara, Mitsuru Takeda, Takumi Takada, Takayuki Hori, Yukiyasu Domae
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4413638658