Startups & Funding
XDOF, just 3 months out of stealth, in talks for an about $1.2B Series B
XDOF, a startup that collects real-world teleoperation data for training robots, is reportedly in late-stage talks for a Series B round at a valuation of about $1.2 billion, led by 8VC.
Less than three months after emerging from stealth, XDOF is in talks for the new round, according to people with knowledge of the deal. TechCrunch reported on the startup’s $70 million Series A in June, which drew participation from Thrive Capital, Andreessen Horowitz, Lux, and Spark Capital. XDOF wasn’t planning to raise again so soon after that round, but its rapid growth — with annualized revenue approaching $50 million — prompted VCs to approach it about a new one, the people said. TechCrunch was unable to learn the total capital being raised or whether the valuation includes the new funding, and the deal’s terms are not final and could still change. XDOF and 8VC didn’t respond to requests for comment.
XDOF was co-founded in 2024 by UC Berkeley researchers Philipp Wu (CEO) and Fred Shentu (CTO). The startup builds data pipelines, collection tools, and annotation systems for frontier AI labs and robotics companies, positioning itself as an outsourced data-supply chain for the robotics industry — something investors now describe as the Scale AI or Mercor for physical robotics.
As a PhD student, Wu studied how robots learn from large datasets and found a lack of large-scale data to work with. He teamed up with Shentu on GELLO, a low-cost teleoperation system letting a human operator remotely control a robotic arm to generate training data, which led to an influential robotics paper and became the foundation for XDOF. Unlike large language models, which trained on the entirety of the internet, physical robots lack an equivalent real-world dataset, making data collection a critical bottleneck for building general-purpose machines. XDOF combines remote robot teleoperation with human collectors wearing sensors to record everyday tasks like folding clothes and flattening boxes, and 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, called ABC. The startup plans to hire and train teams of data collectors worldwide, including teleoperators and egocentric operators who wear body sensors to capture movement data, and says it already works with 20 customers, including several frontier AI labs.
Why it matters
Investor appetite for a company positioning itself as robotics’ data-labeling layer signals that data collection, not just model architecture, is becoming a bottleneck worth funding at billion-dollar valuations.