# Reborn Flywheel for RFM

### Why Robotics Can’t Leap to AGI Directly

Despite rapid progress in hardware, LLM and simulation, today's robotics models remain **narrow, overfit, and fragmented**. Each company develops isolated solutions for specific tasks or hardware, and the field lacks:

* Sufficient high-quality, real-world data
* Scalable training pipelines
* A shared model layer that generalizes across settings

Without solving these bottlenecks, the field cannot leap directly into training general-purpose AGI robots.

### The Reborn Roadmap to AGI Robot

Reborn’s pathway toward Robotic Foundation Models (RFMs) is rooted in a **bottom-up, data-centric methodology**. Instead of attempting to train a general-purpose AGI model from the outset, Reborn adopts a **progressive, scalable strategy** that begins with community participation and culminates in high-performance vertical-domain applications.

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**1. Building Data Foundation with Reborn Data Collection Framework**

The process begins with large-scale human participation. Through **Rebocap™ motion capture**, **VR interaction**, and **first-person video recording via smartphones**, Reborn mobilizes users worldwide to contribute **multimodal data**. This grassroots approach ensures an **unmatched level of diversity, realism, and contextual variety**, setting the foundation for generalizable embodied intelligence.

**2. Training Core Embodied Control Models**

With this data, Reborn first trains **general-purpose control and perception models**, focusing on:

* Human locomotion and full-body pose estimation
* Hand-object interaction and fine manipulation
* Basic task decomposition and sequential motor planning

These models form the **core building blocks of embodied intelligence**—modular capabilities that can be adapted across robot types and environments.

**3. Co-building Vertical Domain Applications**

Once foundational skills are established, Reborn collaborates with **leading humanoid robot manufacturers** such as **Unitree** and **Booster Robotics** to jointly develop **vertical-specific application models**. These include:

* Domestic assistance (navigation + grasping)
* Industrial collaboration (human-robot coordination)
* Interactive education or service robots

Reborn provides the algorithmic and data-driven model architecture, while partners contribute **hardware platforms, deployment environments, and real-world feedback**—accelerating the integration of AI into functional, market-ready robots.

**4. Deployment-Driven Iteration**

As these models are deployed in physical and virtual environments, they generate **real-time interaction data and behavioral edge cases**, which are fed back into the training cycle. This continuous feedback loop enhances both **model robustness and generalizability**, pushing the system closer to the long-term goal of autonomous, general-purpose RFMs.

This iterative pipeline forms the backbone of the **Reborn Flywheel**:

> **Global Data Collection → Core Model Training → Vertical Deployment → Real-World Feedback → Model Refinement → Toward RFMs**

Each cycle drives progress—expanding the model’s capabilities, enriching the data, and reducing the gap between specialized robotics and true embodied AGI.