Gasgoo Munich-BrainCo globally unveiled its BrainCo Dexterous Manipulation Data Matrix at the 2026 World Artificial Intelligence Conference (WAIC 2026). While the industry remains fixated on how robots understand humans, this solution tackles a far more fundamental and thorny challenge: how robots learn to manipulate objects with dexterity.Data is fast becoming the scarcest "mineral" on the path to general embodied intelligence. By early 2026, the world held just 500,000 hours of high-quality embodied data—yet training a general embodied model is expected to require more than 10 million hours. That leaves a gap exceeding 99%.Unlike large language models that can "devour" internet text, data for robotic dexterity must be gathered piece by piece in the physical world. Real-machine teleoperation offers high precision but is too expensive to scale. Human demonstrations are cheaper, yet struggle to balance precision with natural movement. Meanwhile, simulation provides volume but runs into "domain adaptation" hurdles.The industry has been starved of data for far too long.Image Credit: BrainCoThe BrainCo Dexterous Manipulation Data Matrix proposes a "three-layer data system" as the answer.At the top is real-machine data, collected via a dual-arm wheeled platform (RevoTron/RevoMate). It features a 21-degree-of-freedom dexterous hand with full-palm tactile sensing and covers 64 degrees of freedom for whole-body motion control. The system synchronously records head-mounted environmental vision, hand-eye vision, motion status, full-palm tactile feedback, and voice semantics—creating "action-tactile-vision-semantic" multimodal data.Image Credit: BrainCoThe middle layer consists of human-centric data gathered by the RevoHuman high-precision exoskeleton tactile glove. Weighing just 125 grams, it can be donned or doffed in nine seconds. It utilizes a 21-degree-of-freedom encoder paired with 14 thin-filmc tactile sensor modules covering 211 tactile points, achieving angular accuracy of ±0.1° and tactile resolution down to 0.01N.Image Credit: BrainCoThe foundation layer comprises first-person video from the RevoEgo data collection headband and RevoSim simulation data—providing a massive, low-cost source for model pre-training.The strategy follows a dual-track approach: "real machines ensure precision, real humans provide scale." Real-machine data acts as a precision anchor for fine-tuning models, while human-centric data serves as a scale engine to continuously churn out high-quality operational data. The system maintains an open architecture, compatible with mainstream devices like IMU motion-capture gloves and exoskeleton gloves, and adapts to 6- or 7-degree-of-freedom collaborative arms and various robot bodies.As brain-computer interfaces, artificial intelligence, and embodied intelligence converge, the nature of human-robot collaboration is being redefined. And the starting point for it all may just be a single, sufficiently precise stream of dexterous manipulation data.