RynnBrain

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Introduction

We present RynnBrain, an embodied foundation model grounded in physical reality. RynnBrain is available in two dense variants (2B and 8B) and one mixture-of-experts (MoE) model (30B-A3B). In addition, we release three post‑trained models: RynnBrain‑Plan (robot task planning), RynnBrain‑Nav (vision-language navigation), and RynnBrain‑CoP (chain-of-point reasoning).

🌟 Key Highlights

• Comprehensive egocentric understanding: Excels in fine-grained video understanding and egocentric cognition, covering tasks such as embodied QA, counting, and OCR.
• Diverse spatio-temporal localization: Possesses powerful localization capabilities across episodic memory, enabling precise identification of objects, target areas, and motion trajectories.
• Physical-space reasoning: Employs an interleaved reasoning strategy that alternates between textual and spatial grounding, ensuring that its reasoning processes are firmly rooted in the physical environment.
• Physics-aware precise planning: Integrates located affordances and object information into planning, enabling downstream VLA models to execute intricate tasks with fine-grained instructions.