Data startup XDOF nears unicorn status

Less than three months after emerging from stealth, XDOF, a startup that collects real-world teleoperation data for training general-purpose robots, is in late-stage talks to raise a Series B at a valuation of about $1.2 billion, led by 8VC, according to several people with knowledge of the deal.

Rapid growth sparks new round

XDOF was co-founded by UC Berkeley researchers Philipp Wu (CEO) and Fred Shentu (CTO) in 2024. TechCrunch reported on the startup’s $70 million Series A in June, with participation from Thrive Capital, Andreessen Horowitz, Lux, and Spark Capital. XDOF wasn’t planning to raise again so soon after that round. But the company’s rapid growth — with annualized revenue approaching $50 million — prompted VCs to approach it about a new round, the people said.

TechCrunch was unable to learn the total capital being raised or whether the valuation includes the new funding. The terms of the deal are not final and could still change.

Data supply chain for robotics

XDOF aims to build the data pipelines, collection tools, and annotation systems that frontier AI labs and robotics companies can’t easily build themselves, essentially acting as an outsourced data-supply chain for the robotics industry. As a PhD student, Wu was studying how robots learn from large datasets. One big impediment to his research was the lack of “large-scale data to work with,” he told TechCrunch in June.

From GELLO to XDOF

So he teamed up with Shentu on a project called GELLO, a low-cost teleoperation system that allows a human operator to control a robotic arm remotely in order to generate training data. Their work led to an influential paper in robotics. That research formed the foundation for XDOF, which investors now describe as the Scale AI or Mercor for physical robotics, a reference to the data-labeling giants that helped fuel the AI boom. Unlike LLMs, which initially trained on the entirety of the internet, physical robots don’t have an equivalent real-world dataset to draw from, making data collection a critical bottleneck to building general-purpose machines.

Partnership with UC Berkeley

XDOF is partnering with UC Berkeley’s AI Research lab to release what it believes is the largest collection of high-quality robot training data ever assembled, dubbed ABC. To capture this data, XDOF combines remote robot teleoperation with human collectors who wear sensors to record everyday tasks like folding clothes and flattening boxes.

Building a global data collection force

The startup plans to hire and train teams of data collectors worldwide, including teleoperators who steer robots remotely and egocentric operators who wear body sensors to capture movement data. XDOF previously told TechCrunch that it is already working with 20 customers, including several frontier AI labs.

Competitive landscape

Other startups attempting to collect real-world data for robot training include Mecka AI, as well as human-data platforms expanding beyond LLMs, such as Scale AI and Micro1. XDOF’s rapid traction suggests that the demand for physical-world training data is becoming as critical as the text and image data that fueled earlier AI advances. The company’s ability to attract investor interest at such a high valuation only months after its public debut underscores the perceived value of its approach.

Why this matters

The potential $1.2 billion valuation signals that investors see real-world data as the next frontier in AI development, with implications for the entire robotics ecosystem. As XDOF scales, its influence on how robots learn could shape the capabilities and limitations of autonomous systems in industries ranging from manufacturing to home assistance. For companies relying on robotics, having robust data pipelines could be the difference between a prototype and a viable product.