RynnBrain: Open Embodied Foundation Models
RynnBrain aims to serve as a physics-aware embodied brain: it observes egocentric scenes, grounds language to physical space and time, and supports downstream robotic systems with reliable localization and planning outputs.
Comprehensive egocentric understanding
Strong spatial comprehension and egocentric cognition across embodied QA, counting, OCR, and fine-grained video understanding.
Diverse spatiotemporal localization
Locates objects, target areas, and predicts trajectories across long episodic context, enabling global spatial awareness.
Physical-space grounded reasoning (RynnBrain family)
The broader RynnBrain family includes “Thinking” variants that interleave textual reasoning with spatial grounding to anchor reasoning in reality.
Physics-aware precise planning (RynnBrain family)
Integrates localized affordances/areas/objects into planning outputs to provide downstream VLA models with precise instructions.
| Model | Base Model | Huggingface | Modelscope |
|---|---|---|---|
| RynnBrain-2B | Qwen3-VL-2B-Instruct | Link | Link |
| RynnBrain-8B | Qwen3-VL-8B-Instruct | Link | Link |
| RynnBrain-30B-A3B (This Checkpoint) | Qwen3-VL-30B-A3B-Instruct | Link | Link |
| RynnBrain‑CoP-8B | RynnBrain-8B | Link | Link |
| RynnBrain‑Plan-8B | RynnBrain-8B | Link | Link |
| RynnBrain‑Plan-30B-A3B | RynnBrain-30B-A3B | Link | Link |
| RynnBrain‑Nav-8B | RynnBrain-8B | Link | Link |
Minimal dependencies:
pip install transformers==4.57.1
Run text generation:
from transformers import AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained("")
...
Checkout the cookbooks that showcase RynnBrain's capabilities in cognition, localization, reasoning, and planning.
| Category | Cookbook name | Description |
|---|---|---|
| Cognition | 1_spatial_understanding.ipynb | Shows the ability of model for spaital understanding in the video scene. |
| Cognition | 2_object_understanding.ipynb | Shows how the model understands object categories, attributes, and relations and counting ability. |
| Cognition | 3_ocr.ipynb | Examples of optical character recognition and text understanding in videos. |
| Location | 4_object_location.ipynb | Locates specific objects with bounding boxes in an image or video based on instructions. |
| Location | 5_area_location.ipynb | Identifies and marks specified regions by points in an image or video. |
| Location | 6_affordance_location.ipynb | Finds areas or objects with specific affordances in an image of video. |
| Location | 7_trajectory_location.ipynb | Infers and annotates trajectories or motion paths in an image or video. |
| Location | 8_grasp_pose.ipynb | Present the model's abiltiy to predict robotic grasp poses from images. |
model-00002-of-00013.safetensors 5.0 GBmodel-00003-of-00013.safetensors 5.0 GBmodel-00005-of-00013.safetensors 5.0 GBmodel-00008-of-00013.safetensors 5.0 GBmodel-00010-of-00013.safetensors 5.0 GBmodel-00006-of-00013.safetensors 5.0 GBmodel-00011-of-00013.safetensors 5.0 GBmodel-00004-of-00013.safetensors 5.0 GBmodel-00007-of-00013.safetensors 5.0 GBmodel-00009-of-00013.safetensors 5.0 GBmodel-00012-of-00013.safetensors 5.0 GBmodel-00001-of-00013.safetensors 5.0 GBmodel-00013-of-00013.safetensors 2.3 GB