RoboBrain 2.0 Technical Report
Authors: name: BAAI RoboBrain Team, name: Mingyu Cao, name: Huajie Tan, name: Yuheng Ji, name: Minglan Lin, name: Zhiyu Li, name: Zhou Cao, name: Pengwei Wang, name: Enshen Zhou, name: Yi Han, name: Yingbo Tang, name: Xiangqi Xu, name: Wei Guo, name: Yaoxu Lyu, name: Yijie Xu, name: Jiayu Shi, name: Cheng Chi, name: Mengdi Zhao, name: Xiaoshuai Hao, name: Shanyu Rong, name: Zhengliang Cai, name: Bolun Zhang, name: Shuyi Zhang, name: Huaihai Lyu, name: Mengfei Du, name: Lingfeng Zhang, name: Xi Feng, name: Xiaodan Liu, name: Yance Jiao, name: Chenrui He, name: Mengsi Lyu, name: Zhuo Chen, name: Yulong Ao, name: Xue Sun, name: Zheqi He, name: Jingshu Zheng, name: Xi Yang, name: Donghai Shi, name: Kunchang Xie, name: Bochao Zhang, name: Shaokai Nie, name: Chunlei Men, name: Yonghua Lin, name: Zhongyuan Wang, name: Tiejun Huang, name: Shanghang Zhang

We introduce RoboBrain 2.0, our latest generation of embodied vision-language
foundation models, designed to unify perception, reasoning, and planning for
complex embodied tasks in physical environments. It comes in two variants: a
lightweight 7B model and a full-scale 32B model, featuring a heterogeneous
architecture with a vision encoder and a language model. Despite its compact
size, RoboBrain 2.0 achieves strong performance across a wide spectrum of
embodied reasoning tasks. On both spatial and temporal benchmarks, the 32B
variant achieves leading results, surpassing prior open-source and proprietary
models. In particular, it supports key real-world embodied AI capabilities,
including spatial understanding (e.g., affordance prediction, spatial
referring, trajectory forecasting) and temporal decision-making (e.g.,
closed-loop interaction, multi-agent long-horizon planning, and scene graph
updating). This report details the model architecture, data construction,
multi-stage training strategies, infrastructure and practical applications. We
hope RoboBrain 2.0 advances embodied AI research and serves as a practical step
toward building generalist embodied agents. The code, checkpoint and benchmark
are available at https://superrobobrain.github.io.