WRC 2026 Data Wars: 1,900 Autonomous Mining Trucks Generate the Industry's Most Coveted "Dirty Data" At the World Robot Conference (WRC 2026) in Beijing's Yizhuang this August, humanoid robots sorted packages on conveyor belts and tightened screws at workstations—such "getting-things-done" scenarios have become as commonplace as theatrical demonstrations. But beneath this bright line of "deployment," a hidden thread about data is quietly determining the next phase of competition in the embodied intelligence industry. Unitree Robotics founder Wang Xingxing said something at the conference that nearly every vendor privately acknowledges: "However much data you have—especially high-quality data—that's how much AI capability you have." Humanoid robots' limbs can already run, jump, and do backflips, but their "brains" are still starving. One frequently cited statistic: China currently has only 500,000 hours of compliant real-world physical interaction data, while commercial robot deployment requires tens of millions of hours—a gap exceeding 99%. From hundreds of thousands to tens of millions, the difference spans two orders of magnitude. So players at WRC each showed their hand: some open-sourced human behavior data, some sold data-collection gloves, some bet on simulation, and some insisted on real-machine, real-world data. Beyond the noise, a few embodied intelligence companies with no booths and little attention are holding the industry's most scarce data. The Open-Source Camp: Turning Data Collection into a "Mass Movement" Lightwheel Intelligence made one of the biggest data-related moves at this year's WRC. On August 20, the company released EgoSuite-Open100K, the world's first 100,000-hour-class full-modal open-source human behavior dataset, covering 7 major environment categories, 128 scenario types, over 15,000 collection scenes, and more than 15,000 tasks. The data is primarily captured from a head-mounted first-person perspective, with some wrist cameras added, providing hand pose, full-body pose, and event-level semantic annotations. The first batch is already available for download via