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Green-VLA: An Open Guide to Building Robotic Management Architecture
In anticipation of Russian Science Day, we published the technical report Green-VLA, dedicated to the development of key physical artificial intelligence technology - Vision-Language-Action models that enable robots to understand the surrounding world, interpret instructions, and turn them into meaningful physical actions. The material ranked first among the articles of the day on the Hugging Face portal, surpassing works by Moonshot AI and collaborative research between Chinese and American universities.
Green‑VLA, built on the basis of the GigaChat neural network, describes a practical approach to training such models, from basic training to tuning the robot's behavior in real-world conditions. The focus is not on a single demonstration but on a comprehensive methodology that can be used by researchers and engineers to create reliable robotic systems.
Physical AI is a dynamically evolving field. Modern robots demonstrate a wide range of capabilities; however, key challenges for their further progress remain increasing stability, ensuring cross-platform interaction, and performing complex multi-step operations. Green‑VLA offers a systematic approach to solving these challenges. It is based on measurable and engineering-validated principles of training robot control systems.
The effectiveness of the approach is confirmed by SOTA results both in practice and in international benchmarks Simpler Fractal and Simpler widowX (Stanford University and Google), as well as CALVIN (University of Freiburg). At the international conference AI Journey 2025, the Green robot, operated by Green‑VLA, worked continuously for over 10 hours, performing tasks without noticeable failures or degradation in behavior.
The VLA technology is becoming the "brain" of physical artificial intelligence: Vision Action Language models turn vision and language into executable action. It is such solutions that helped us create our own AI robot. At Green-VLA, we show how to make this layer engineering-reliable: with portability between robots and behavior alignment through reinforcement learning, so that the model works not only in demo but also in reproducible scenarios and benchmarks. We plan to share our developments to promote the domestic AI and robotics ecosystem, providing researchers and engineers with a tool to create innovative solutions.
The Green-VLA model is viewed as another step towards forming the technological stack of Physical AI, where VLA models become the link between perception of the world, understanding tasks, and physical action. This approach paves the way for creating more autonomous, resilient, and versatile robotic solutions.
Green-VLA is positioned as an open training methodology, rather than a ready-made universal controller for robots. The architecture of the solution assumes a basic pre-training phase followed by adaptation to the target robotic system, which defines its flexibility and potential for scaling.
The report can be reviewed on arXiv and Hugging Face.
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