> For the complete documentation index, see [llms.txt](https://docs.4dlabs.space/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.4dlabs.space/what-is-4dlabs.md).

# What is 4Dlabs

A new embodied-data infrastructure anchored in the real world — high physical consistency, blending real-world and simulated data — combined with a Web3 protocol to power a data production network at scale.

4D Labs defines its mission as follows — **turn the real world into a training ground for robots**. Our core proposition: the everyday operations and professional work that humans already perform continuously in the physical world inherently contain every information dimension a robot needs to learn — vision, motion trajectories, force feedback, and task outcomes. What has long been missing is a systematic mechanism to convert this scattered, implicit behavioral data into structured, trainable fuel for embodied intelligence.

 

\- **Every operation by a human expert** — captured through Ego first-person collection devices and the crowdsourced collect-and-train network — becomes high-fidelity demonstration data that robots can learn from;

\- **Every task a robot performs in a real-world setting** — fed back through the real-robot teleoperation data loop — becomes a training sample for the next generation of models;

\- **Every high-value task in an industry customer's workflow** — simultaneously serves as a data production node, a model validation node, and a scenario reuse node.

 

**Phase One — Building the Embodied Data Infrastructure:** 

With Ego first-person collection devices as the core hardware entry point, layered with the scaled coverage of the crowdsourced collect-and-train network and the high-fidelity data flowing back from real-robot teleoperation, we build complete infrastructure spanning data governance, cleaning, annotation, and training pipelines.

 

**Phase Two — The Model Store Strategic Market:** 

Once data assets reach a critical threshold of scale and quality, the company's focus evolves from "data infrastructure provider" to "distribution platform for reusable model capabilities" — selling trained embodied-intelligence model capabilities as standardized products to downstream customers via APIs and licensed deployment.

 

Unlocking the physical world, building the critical infrastructure for embodied intelligence at scale.