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
Published: 2025-07-02 17:05:33

Abstract: 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.