Green-VLA: Staged Vision-Language-Action Model for Generalist Robots
Authors: name: I. Apanasevich, name: M. Artemyev, name: R. Babakyan, name: P. Fedotova, name: D. Grankin, name: E. Kupryashin, name: A. Misailidi, name: D. Nerus, name: A. Nutalapati, name: G. Sidorov, name: I. Efremov, name: M. Gerasyov, name: D. Pikurov, name: Y. Senchenko, name: S. Davidenko, name: D. Kulikov, name: M. Sultankin, name: K. Askarbek, name: O. Shamanin, name: D. Statovoy, name: E. Zalyaev, name: I. Zorin, name: A. Letkin, name: E. Rusakov, name: A. Silchenko, name: V. Vorobyov, name: S. Sobolnikov, name: A. Postnikov
Organization: Sber Robotics Center

We introduce Green-VLA, a staged Vision-Language-Action (VLA) framework for real-world deployment on the Green humanoid robot while maintaining generalization across diverse embodiments. Green-VLA follows a five stage curriculum: (L0) foundational VLMs, (L1) multimodal grounding, (R0) multi-embodiment pretraining, (R1) embodiment-specific adaptation, and (R2) reinforcement-learning (RL) policy alignment. We couple a scalable data-processing pipeline (3,000 hours of demonstrations) with temporal alignment and quality filtering, and use a unified, embodiment-aware action interface enabling a single policy to control humanoids, mobile manipulators, and fixed-base arms. At inference, the VLA controller is enhanced with episode-progress prediction, out-of-distribution detection, and joint-prediction-based guidance to improve safety and precise target selection. Experiments on Simpler BRIDGE WidowX and CALVIN ABC-D, as well as real-robot evaluations, show strong generalization and performance gains from RL alignment in success rate, robustness, and long-horizon efficiency.