> 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. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.4dlabs.space/what-is-4dlabs.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.