About
Last updated
Last updated
Embodied AI is the foundation of artificial general intelligence (AGI) for robots—combining perception, reasoning, and action to enable machines to operate in the physical world. Unlike traditional AI, which is limited to text or image processing, embodied AI powers real-world interactions through robotic or simulated bodies, making it essential for the development of general-purpose humanoid robots.
Reborn Network is building the decentralized protocol for this future: a foundational layer for the open ecosystem of AGI robots. We aim to transform how robots are trained—not in silos, but by tapping into the collective intelligence of humanity. By enabling the global community to contribute human motion data from VR/AR gaming, motion capture using our affordable Rebocap™ hardware, and real-world task videos via smartphones, Reborn turns everyday activities into tokenized assets for training robotic foundation models (RFMs).
This community-powered model training loop is governed on-chain—rewarding contributors with Reborn tokens, promoting open access to physical AI model, and ensuring that the future of robotics is shaped by the People, for the People.
The path to building truly general-purpose humanoid robots is constrained by three deeply interconnected and persistent bottlenecks: the data gap, stemming from a lack of large-scale, diverse, and high-quality motion data; the model gap, where existing AI models remain overfitted and narrowly applicable due to insufficient training signals; and the embodiment gap, driven by the wide variation in robot hardware designs that require platform-specific tuning and make scalable deployment of universal intelligence extremely challenging.
The extraordinary capabilities of modern large models in language and vision stem from their training on vast datasets, freely sourced from decades of internet content contributed by billions of users and communities. Language models, for instance, are trained on 15 trillion tokens, while vision models leverage 6 billion images to achieve remarkable performance. In stark contrast, robotics development is severely constrained by data scarcity. Scientists and robotics leaders, such as Tesla, are striving to build the next generation of general-purpose humanoid robots (AGI Humanoid Robots). These robots require the training of embodied AI models, which depend on massive amounts of human motion data. Currently, the available datasets are severely limited:
Real-World Lab Data: Mostly collected by research institutions at an exorbitant cost, with the largest real-world dataset containing only 2.4 million samples.
Simulator Data: Often generated in virtual environments, easily scaled up yet failing to fully align with the complexities and physical laws of the real world.
Robotics data scaling laws highlight that achieving truly general-purpose robots requires scaling both the quantity and diversity of data. Without this, the vision of a universal humanoid robot remains unattainable.
Even with recent advances in imitation learning, reinforcement learning, and vision-language-action (VLA) models, current embodied AI models remain narrow, brittle, and overfitted. Due to the lack of large-scale, diverse, and multimodal training data, most models are only effective in tightly constrained environments or with specific hardware. As visualized in the diagram, while data is meant to feed models, today's model pipelines suffer from insufficient generalization capacity—limiting their ability to scale across different tasks, scenes, or embodiments. Without high-volume, heterogeneous training data that spans ego-view, real-world, synthetic, and motion domains, these models fail to capture the full complexity of human-like intelligence required by AGI robots.
The final barrier lies in the hardware reality of robots. Each robot platform—be it Unitree H1, Figure 02, or Tesla Optimus—comes with distinct morphology, payloads, kinematics, and control limitations. As shown in the “Embodiment” module of the diagram, even if the intelligence layer (models) is ready, deploying it consistently across different embodiments is technically and economically prohibitive.
Most robotics companies today are forced to collect and train on their own data in-house, due to incompatibility with shared models or lack of relevant datasets. This creates high costs, fragmented efforts, and a lack of knowledge transfer between platforms. The embodiment gap, therefore, is not just about mechanical diversity—but about the absence of a unified interface between intelligence and physical form.
The market for intelligent robots is projected to exceed $500B in the coming decades, transforming sectors from healthcare and logistics to personal services and eldercare. Yet, over 30% of R&D budgets for leading robotics firms are still spent on data collection and annotation, highlighting the urgent need for scalable, cost-effective solutions.
To address the three key bottlenecks, Reborn introduces a new paradigm—where anyone can contribute, own, and benefit from the robotic intelligence of the future.
Reborn Network decentralizes the pipeline for training various Robotic Foundation Models (RFMs) by turning everyday human motion into valuable training signals. Our four key data engines include:
Embodied Vlog: Real-world first-person videos of daily tasks and fine-grained manipulation.
Mocap Life: High-fidelity motion data collected using low-cost Rebocap™ wearable devices.
VR Gaming: Task-driven interaction data from immersive virtual environments.
Roboverse Simulation: A high-fidelity sim engine aligned with physical laws, used to generate synthetic training data for embodied control, exploration, and vision-language reasoning.
These multimodal data streams are curated and validated via community consensus, secured by blockchain, and used to train Robotic Foundation Models that generalize across embodiments and tasks.
Alongside the data layer, Reborn maintains a growing Versatile Physical AI Model Zoo, co-developed with leading robotic companies such as Unitree. This model zoo includes three main streams:
OpenVLA models for vision-language-action reasoning,
Full-body control policies for humanoids,
Dexterous manipulation agents, and more.
These models are trained using Reborn data, optimized for deployment on real-world robots, and accessible through the Reborn platform, forming the building blocks of the future AGI robot stack.
Contributors to this ecosystem are rewarded in Reborn tokens, gaining ownership in the robotic intelligence they help create. With 200,000+ monthly active users, 8,000+ Rebocap™ units sold, and a growing model and data network, Reborn is laying the foundation for a future where robotics is open, decentralized, and community-owned.
