In this work, we introduce HoloBrain-0, a comprehensive Vision-Language-Action (VLA) framework that bridges the gap between foundation model research and reliable real-world robot deployment. The core of our system is a novel VLA architecture that explicitly incorporates robot embodiment priors, including multi-view camera parameters and kinematic descriptions (URDF), to enhance 3D spatial reasoning and support diverse embodiments. We validate this design through a scalable ``pre-train then post-train" paradigm, achieving state-of-the-art results on simulation benchmarks such as RoboTwin 2.0, LIBERO, and GenieSim, as well as strong results on challenging long-horizon real-world manipulation tasks. Notably, our efficient 0.2B-parameter variant rivals significantly larger baselines, enabling low-latency on-device deployment. To further accelerate research and practical adoption, we fully open-source the entire HoloBrain ecosystem, which includes: (1) powerful pre-trained VLA foundations; (2) post-trained checkpoints for multiple simulation suites and real-world tasks; and (3) RoboOrchard, a full-stack VLA infrastructure for data curation, model training and deployment. Together with standardized data collection protocols, this release provides the community with a complete, reproducible path toward high-performance robotic manipulation.
@article{arxiv.2602.12062,
title = {HoloBrain-0 Technical Report},
author = {Xuewu Lin and Tianwei Lin and Yun Du and Hongyu Xie and Yiwei Jin and Jiawei Li and Shijie Wu and Qingze Wang and Mengdi Li and Mengao Zhao and Ziang Li and Chaodong Huang and Hongzhe Bi and Lichao Huang and Zhizhong Su},
journal= {arXiv preprint arXiv:2602.12062},
year = {2026}
}