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.
@article{arxiv.2602.00919,
title = {Green-VLA: Staged Vision-Language-Action Model for Generalist Robots},
author = {I. Apanasevich and M. Artemyev and R. Babakyan and P. Fedotova and D. Grankin and E. Kupryashin and A. Misailidi and D. Nerus and A. Nutalapati and G. Sidorov and I. Efremov and M. Gerasyov and D. Pikurov and Y. Senchenko and S. Davidenko and D. Kulikov and M. Sultankin and K. Askarbek and O. Shamanin and D. Statovoy and E. Zalyaev and I. Zorin and A. Letkin and E. Rusakov and A. Silchenko and V. Vorobyov and S. Sobolnikov and A. Postnikov},
journal= {arXiv preprint arXiv:2602.00919},
year = {2026}
}