The flexible tactile sensing company has received a new round of financing, and the company’s revenue is expected to increase 10 times in 2026

Author｜Huang Nan Editor｜Yuan Silai Hard Krypton has learned that the flexible tactile sensing company "Yolao Technology" has recently completed a new round of Pre-A+ financing. This round of financing was led by Dinghe Gouda, with listed companies Changshu Automotive Decoration and Zulong Entertainment following the investment, and Yundao Capital served as the long-term exclusive financial advisor. The funds will be mainly used for the research and development of flexible fabric sensing technology, iterative upgrade of product performance, accelerating the expansion of data glove production capacity and batch delivery, and improving data collection, calibration and interface capabilities to accelerate the implementation of scenarios such as embodied intelligence, world models and smart cockpits. Yaole Technology uses flexible fabric sensing as the hardware entrance to collect and preprocess real physical interaction data required for embodied intelligence and world models. Founder and CEO Lu Liyun was the chief architecture engineer of Harman, a leading international automotive electronics company, and led the research and development of multi-modal sensor fusion computing platforms. Most of the core team members come from leading universities such as the Chinese Academy of Sciences, University of Michigan, and Peking University, covering the entire chain of research and development capabilities from bottom-level sensing materials to upper-level intelligent algorithms. As hardware engineering such as humanoid robot bodies, motors, and joint control have made breakthroughs one by one, industry competition has undergone phased transitions, and the deep constraints come from the structural shortage of data supply. According to the "China Embodied Intelligence Industry Development Report" report, as of the beginning of 2026, the total amount of high-quality real physical interaction data in the world is about 500,000 hours, and training a universal embodied model with basic generalization capabilities requires at least tens of millions of hours of data, leaving a gap of more than 90%. Compared with visual and text training materials that can be collected on a large scale through online channels, the tactile modality has a unique supply bottleneck. Tactile signals originate from direct physical contact between entities. The simulation environment can simulate the shape and position of objects, but it is difficult to reproduce the composite mechanical characteristics such as pressure distribution, friction, and material deformation in real scenes. There are inherent deviations between the simulation data set and the real tactile values, and it cannot be used as high-quality training material to completely replace real-life acquisition data. This also forces the industry to find new ways to obtain data from the physical side. As a physical interface connecting human operation and robot learning, digital mining gloves can not only capture contact mechanics signals in real operations, but also implement them in a relatively standardized form.