Less than three months after coming out of stealth, XDOFA startup that collects real-world teleoperation data to train general-purpose robots, is in final talks to raise a Series B at a valuation of about $1.2 billion led by 8VC, multiple people with knowledge of the deal said.
XDOF was co-founded by UC Berkeley researchers Philipp Wu (CEO) and Fred Shentu (CTO) in 2024. TechCrunch reported on the startup $70 million Series A in June, with the participation of Thrive Capital, Andreessen Horowitz, Lux and Spark Capital. XDOF wasn’t planning on re-raising so soon after that round. But the company’s rapid growth (with annualized revenue approaching $50 million) led venture capitalists to approach it for a new round, the people said.
TechCrunch was unable to learn the total capital being raised or whether the valuation includes the new financing. The terms of the agreement are not final and could still change.
XDOF and 8VC did not respond to our request for comment.
The startup aims to build data pipelines, collection tools, and annotation systems that AI labs and cutting-edge robotics companies can’t easily build themselves, essentially acting as an outsourced data supply chain for the robotics industry.
As a doctoral student, Wu was studying how robots learn from large data sets. A big impediment to his research was the lack of “large-scale data to work with,” he told TechCrunch in June.
So he partnered with Shentu on a project called GELLO, a low-cost teleoperation system that allows a human operator to control a robotic arm remotely to generate training data. Their work led to an influential paper in robotics.
That research formed the basis of XDOF, which investors now describe as Scale AI or Mercor for physical robotics, a reference to the data labeling giants that helped fuel the AI boom. Unlike LLMs, which were initially trained on the Internet, physical robots do not have an equivalent real-world data set to draw from, making data collection a critical bottleneck for building general-purpose machines.
XDOF is partnering with UC Berkeley’s artificial intelligence research lab to publish what it believes is the largest collection of high-quality robot training data ever assembled, called alphabet.
To capture this data, XDOF combines remote teleoperation of robots with human pickers who use sensors to record everyday tasks such as folding clothes and flattening boxes.
The startup plans to hire and train teams of data collectors around the world, including teleoperators who direct robots remotely and egocentric operators who use body sensors to capture motion data.
XDOF previously told TechCrunch that it is already working with 20 clients, including several cutting-edge AI labs.
Other startups trying to collect real-world data for robot training include Mecka AI, as well as human data platforms expanding beyond LLMs, such as Scaling AI and Micro1.
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