At 1,200 items per hour with a 95% accuracy rate, operating 24/7, the once flashy "show-off" robots that danced and flipped at exhibitions have now become new colleagues on the job at the Guangzhou Postal Processing Center. In the first half of 2026, total financing in the domestic embodied intelligence sector exceeded 93.5 billion yuan, marking a fivefold increase year-on-year. Shifting from "stunts" to "real work," embodied intelligence robots are accelerating their entry into factories.

Robots "Clock In" at Factories Hit the Accelerator

At the mail sorting line of the Jianggao Land Transport Export Center at the Guangzhou Postal Processing Center, several embodied intelligence robots are swinging their arms, precisely completing tasks like label recognition, parcel feeding, and sorting of abnormal items. After more than five months of continuous optimization, each robot's feeding efficiency has jumped from an initial 300 pieces per hour to a maximum of 1,200 pieces per hour, with an accuracy rate exceeding 95% and the ability to operate around the clock. "This position is physically demanding and involves repetitive work," said Wang Lei, head of the operation and maintenance center at the Guangzhou Postal Processing Center. He expects that by the fourth quarter of this year, robot efficiency will reach 1,400 pieces per hour per unit, and aim for 1,600 pieces per hour in the first quarter of next year. Meanwhile, they are also exploring the extension of robot applications in loading, unloading, and warehousing.

This is not an isolated case. At an automotive factory in Beijing's Yizhuang area, embodied intelligence robots are following the production line rhythm, practicing fine operations like picking and placing parts, positioning flexible covers, and folding material boxes. In a single workstation, they achieve a 98% success rate. From mail sorting to car manufacturing, from 3C electronics to power battery production lines, robots are accelerating their "factory work" this year.

Investment Logic Shifts: From "Selling Concepts" to "Watching Deployment"

Behind the acceleration of robots "clocking in" at factories, capital is also betting heavily on this track. In the first half of 2026, the total financing in the domestic embodied intelligence sector broke through 93.5 billion yuan, a fivefold increase year-on-year. But the investment logic is changing: whether a robotics company can achieve engineering delivery, realize large-scale application, and possess the ability to continuously secure orders has become a key indicator for investment institutions. Alongside the shift in primary market investment logic, the embodied intelligence robotics industry is moving from an early technology verification phase toward a commercialization phase. Huang Jing, general manager of the robot product line at Topstar Technology, divides product deployment into three stages: the first stage is industrial scene deployment, the second is commercial scenes, and the third is home scenes.

How to Solve the Data Problem? Getting the "Data Flywheel" Spinning

Getting robots to "work in factories" smoothly is not as simple as imagined. A lack of real-world scene data is a major obstacle. Unlike large language models that can be trained using publicly available text data from the internet, embodied intelligence robots need to learn the physical laws of the real world, including grasping methods and force control for objects of different materials, as well as motion feedback in complex environments. This data from factory production lines is often highly confidential and not publicly circulated. How to crack the data problem? The industry is seeking new paths: letting robots enter real scenarios, continuously accumulating data while working, then using that data to improve robot capabilities. Enhanced robots then enter more application scenarios, generating even more data. Through this ongoing cycle, robots achieve continuous evolution. This is what industry insiders call the "data flywheel." Across the country, many tech companies are conducting such explorations, hoping to refine technology through real-world scenarios, gradually form a "data flywheel" during robot deployment, and then migrate through scenario training to ultimately achieve cross-scenario applications. "The entire industry has realized that the true value of technology lies in the scenarios, in real demand," said Sun Teng, CEO of Shenzhen Ruozhi Technology Co., Ltd., a sentiment shared by many peers.

Policy Support, Large-Scale Deployment of Tens of Thousands of Units on the Horizon

Behind the accelerated industry deployment is policy support. In June, the Ministry of Industry and Information Technology (MIIT) and the State-owned Assets Supervision and Administration Commission (SASAC) jointly launched a special action for humanoid robots and embodied intelligence real-world training. The goal is to consolidate over 100 high-value application scenarios by the end of 2026, driving the formation of a large-scale deployment capability for tens of thousands of units. From exhibition booths to workshops, from "seeing whose moves are cooler" to "seeing who works more steadily," the embodied intelligence robotics industry is undergoing a paradigm shift from "stunts" to "real work." What is visible from the 93.5 billion yuan in financing is not another concept trend, but a clear path to commercialization: real scenarios generate real data, real data trains stronger capabilities, and stronger capabilities unlock more scenarios. When this "data flywheel" starts spinning, robots are no longer exhibits in showrooms but productive forces on the production line. The target for large-scale deployment of tens of thousands of units has been set, and the list of hundreds of high-value scenarios is growing. Whoever can first run the "data flywheel" in real industrial scenarios will likely gain an edge in this competition of the "real work era."