Abstract
We present RynnBrain, an embodied foundation model grounded in physical reality. Moving beyond passive observation, RynnBrain anchors its understanding in the physical world through comprehensive egocentric cognition, precise spatiotemporal grounding, and real task planning. This systematic upgrade enables active, physics-aware reasoning and complex task execution.
We release RynnBrain in dense (2B, 8B) and MoE (30B) variants, alongside three specialized models: RynnBrain‑Plan (manipulation planning), RynnBrain‑Nav (navigation), and RynnBrain‑CoP (spatial reasoning).
🌟 Key Highlights
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Comprehensive Egocentric Understanding:
Excels in fine-grained video understanding and egocentric cognition, covering diverse tasks such as embodied QA, counting, and OCR. -
Diverse Spatiotemporal Localization:
Possesses powerful localization capabilities across episodic memory, enabling precise identification of objects, target areas, and motion trajectories. -
Physical-Space Reasoning:
Interleaves textual and spatial grounding to anchor reasoning firmly in physical reality. -
Physics-Aware Planning:
Integrates localized affordances and object info into planning, enabling downstream VLA models to execute intricate tasks.
Overview
Results
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Skill Demonstration
Showcasing specific capabilities.
Real-Robot Demos
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Navigation Demos
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Model Architecture
RynnBrain Bench
BibTeX
@article{rynnbrain2026,
title = {RynnBrain: Open Embodied Foundation Models},
author = {Ronghao Dang and Jiayan Guo and Bohan Hou and Sicong Leng and Kehan Li and Xin Li and Jiangping Liu and Yunxuan Mao and Zhikai Wang and Yuqian Yuan and Minghao Zhu and Xiao Lin and Yang Bai and Qian Jiang and Yaxi Zhao and Minghua Zeng and Junlong Gao and Yuming Jiang and Jun Cen and Siteng Huang and Liuyi Wang and Wenqiao Zhang and Chengju Liu and Jianfei Yang and Shijian Lu and Deli Zhao},
year = {2026}
}