RynnBrain: Open Embodied Foundation Models
Authors: name: Ronghao Dang, name: Jiayan Guo, name: Bohan Hou, name: Sicong Leng, name: Kehan Li, name: Xin Li, name: Jiangpin Liu, name: Yunxuan Mao, name: Zhikai Wang, name: Yuqian Yuan, name: Minghao Zhu, name: Xiao Lin, name: Yang Bai, name: Qian Jiang, name: Yaxi Zhao, name: Minghua Zeng, name: Junlong Gao, name: Yuming Jiang, name: Jun Cen, name: Siteng Huang, name: Liuyi Wang, name: Wenqiao Zhang, name: Chengju Liu, name: Jianfei Yang, name: Shijian Lu, name: Deli Zhao
Organization: DAMO Academy

Despite rapid progress in multimodal foundation models, embodied intelligence community still lacks a unified, physically grounded foundation model that integrates perception, reasoning, and planning within real-world spatial-temporal dynamics. We introduce RynnBrain, an open-source spatiotemporal foundation model for embodied intelligence. RynnBrain strengthens four core capabilities in a unified framework: comprehensive egocentric understanding, diverse spatiotemporal localization, physically grounded reasoning, and physics-aware planning. The RynnBrain family comprises three foundation model scales (2B, 8B, and 30B-A3B MoE) and four post-trained variants tailored for downstream embodied tasks (i.e., RynnBrain-Nav, RynnBrain-Plan, and RynnBrain-VLA) or complex spatial reasoning tasks (i.e., RynnBrain-CoP). In terms of extensive evaluations on 20 embodied benchmarks and 8 general vision understanding benchmarks, our RynnBrain foundation models largely outperform existing embodied foundation models by a significant margin. The post-trained model suite further substantiates two key potentials of the RynnBrain foundation model: (i) enabling physically grounded reasoning and planning, and (ii) serving as a strong pretrained backbone that can be efficiently adapted to diverse embodied tasks.