RynnBrain: Open Embodied Foundation Models
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.
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Ronghao Dang
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Ronghao Dang
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Jiayan Guo
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Jiayan Guo
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Bohan Hou
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Sicong Leng
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Leng Sicong
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Kehan Li
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Kehan Li
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Xin Li
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Jiangpin Liu
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Yunxuan Mao
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Minghao Zhu
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Minghao Zhu
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Xiao Lin
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Xiao Lin
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Yang Bai
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Qian Jiang
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Yaxi Zhao
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Yaxi Zhao
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Minghua Zeng
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Junlong Gao
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junlong gao
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Yuming Jiang
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Siteng Huang
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Siteng Huang
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Deli Zhao
RynnBrain: Open Embodied Foundation Models
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.
RynnBrain is an open-source spatiotemporal foundation model for embodied intelligence that unifies perception, reasoning, and planning capabilities across multiple scales and task-specific variants.
multimodal foundation models embodied intelligence spatiotemporal foundation model egocentric understanding spatiotemporal localization physically grounded reasoning physics-aware planning MoE post-trained variants embodied foundation models vision understanding benchmarks