The theme of "practical work" has become the defining keyword for China's embodied intelligence industry in 2026. Robots are no longer confined to dancing or performing somersaults on exhibition stages; instead, they are now entering factory workshops and pharmacy warehouses, effectively "clocking in for work." The transition from "being able to perform" to "being able to work" signals a profound industrial transformation underway in the sector.

The validation phase of crossing from "performance capability" to "work capability."

At the Embodied Intelligence Future Island in Hangzhou's West Science and Technology Innovation Corridor, four machines recently "graduated" from a specialized "robot school." One has been hired as a docent at the China历代绘画大系 (Chinese Paintings Collection) archive, another remains at the Hangzhou facility for educational purposes, a third is now conducting inspections at a Nestlé workshop in Tianjin, while a piano-playing robot is set to travel to South Korea for entertainment performances. Prior to these latest graduates, their predecessors had already assumed roles as workers within factory environments.

On the mass production lines at CATL (Contemporary Amperex Technology Co., Ltd.), the heavy-duty humanoid robot Galbot S1 from Galaxy General lifts crates weighing dozens of kilograms with both arms, delivering them with precision to the next workstation. Since passing acceptance checks in March, it has operated continuously for over three months on a 24/7 basis, navigating complex environments autonomously using only a pure vision system. This marks the world's first instance of routine autonomous operation by a humanoid robot in a new energy intelligent manufacturing setting. To date, Galaxy General's commercial deployment has surpassed one thousand units, serving leading manufacturers such as CATL, Bosch, Toyota, and SAIC Motor.

At the Longqi Technology factory in Nanchang, Jiangxi province, eight Zhiyuan Spirit G2 robots are managing the entire tablet quality inspection process, independently completing high-precision checks for multimedia, audio, and Wi-Fi coupling while coordinating with human line workers. According to Ai Wen, project director of Zhiyuan's Genie business unit, these robots achieve 80% to 90% of human worker efficiency and have logged over 3,000 hours of continuous operation.

Embodied intelligence is also extending into daily service applications. In May, Fox News anchor Bret Baier interacted with a robot clerk at a Beijing convenience store, drawing attention from international media. Tian Feng, president of the Kuaisi Research Institute and former head of SenseTime's Intelligent Industry Research Institute, noted that the industry is currently in a verification period moving from "performance capability" to "work capability." While performances may impress audiences, formal work demands satisfaction from production managers.

The next phase: advancing from "being able to do" to "doing well."

Embodied intelligence represents an artificial intelligence system housed within a physical entity, with its critical value lying in whether it can enter real environments, establish a complete "perception-cognition-decision-action-feedback" loop, and continuously learn and improve. It is important to note that embodied intelligence does not inherently mean humanoid robots; the humanoid form serves only as one possible carrier. The evaluation standard is not how closely it resembles a human, but whether it can resolve practical problems.

A complete embodied system operates through the coordination of three layers: perception, decision-making, and execution. The perception layer functions as the "senses," incorporating cameras, LiDAR, torque sensors, tactile sensors, and inertial measurement units. The decision-making layer acts as the "brain," relying on chips, algorithms, and operating systems, with the mainstream approach being the collaboration of "large and small brains": large models handle understanding and planning, while motion control algorithms manage real-time control. The execution layer serves as the "body," comprising motors, reducers, joint modules, and actuators. Combined, actuators and joint modules account for 45% to 65% of a humanoid robot's total cost, with precision and reliability determining whether a robot can "grip firmly and perform well."

Tian Feng highlighted that China's competitive edge lies in integrating hardware, real factory requirements, rapid supplier response, and patient capital into a complete closed loop. This closed loop is currently being tested in actual factory settings. China follows a model focused on scenario-driven AI construction, small-batch complete machine delivery, and full-chain domestic supply. Unlike overseas approaches that first build a general-purpose "brain" and then seek applications, Chinese developers work backward from specific job requirements to define model capabilities. The confidence in this approach stems from the supply chain: motor, reducer, and sensor manufacturers, along with complete machine builders and downstream factories, are concentrated within the same industrial belt. When a factory reports instability in the robot's grip, suppliers can have a revised version ready for testing within a month. This dense supply chain has dramatically reduced costs: a motor that cost 50,000 to 60,000 yuan in 2018 could now cost approximately 500 to 600 yuan by 2026.

Analysts suggest that when machines are placed in real work scenarios and handle genuine failures, useful data and reliable hardware emerge most rapidly. However, significant challenges remain. "Embodied AI faces a data scarcity problem different from large language models," said one industry expert. "Internet text is essentially free, but physical interaction data must be generated in the real world, which is costly. AI models still struggle to generalize to unfamiliar environments."

Wang Xingxing, founder of Unitree Robotics, provided a quantitative benchmark at the 2026 World Robot Conference in August: when a general-purpose robot can complete approximately 80% of tasks in 80% of unfamiliar scenarios through voice or text commands, the industry will reach an explosive tipping point. He acknowledged that the "ChatGPT moment" for embodied intelligence could ideally arrive in two to three years, or potentially take five to ten years.

Beyond data scarcity, industry insiders have identified the next phase goal: moving from "being able to do" to "doing well." "Current applications prove the technology has entered a more demanding stage. Success, however, is not measured by whether videos go viral, but by whether the machine can return to the same workstation the next day and reliably perform the same tasks at an acceptable cost," Tian Feng stated. Analysts indicate that robots' capability boundaries are being pushed by real tasks, but breakthroughs have yet to be achieved. This is precisely the logic behind scenario-driven model construction: first expose problems through real-world scenarios, then rely on the supply chain to rapidly resolve them.

Coordinated policy guidance and infrastructure development.

Training ground construction is accelerating to address the data shortage, core component localization is driving down costs, and special initiatives for real-scenario training are bridging the "last millimeter" from laboratory to production line. Behind each obstacle overcome lies a corresponding national or industrial-level strategy. At the national level, the "15th Five-Year Plan" outlines forward-looking deployment of future industries, promoting quantum technology, biomanufacturing, hydrogen and nuclear fusion energy, brain-computer interfaces, embodied intelligence, and sixth-generation mobile communications as new economic growth points. Analysts believe that under this top-level design, execution and perception hardware will be the first to benefit.

In June, China's Ministry of Industry and Information Technology and the State-owned Assets Supervision and Administration Commission jointly launched the 2026 special action for real-scenario training of humanoid robots and embodied intelligence, aiming to develop over 100 high-value application scenarios by the end of 2026, driving the formation of ten-thousand-unit-scale deployment capabilities, and accelerating the construction of a "real-scenario training, data accumulation, product iteration, and scaled deployment" cycle.

Local governments are also advancing initiatives. On July 28, the Shunde District Embodied Intelligence Development Bureau was inaugurated in Foshan, Guangdong province, becoming China's first governmental body dedicated to coordinating the entire embodied intelligence industry chain. Shunde is home to the country's largest industrial robot production base, with the district's robotics industry output value reaching nearly 40 billion yuan in 2025, gathering over one hundred upstream and downstream enterprises and forming a complete industry chain covering components and complete machines.

In the first half of this year, China accounted for 97% of global humanoid robot shipments. By the end of June 2026, more than 70 embodied intelligence training grounds had been established and put into operation nationwide. Beijing's Humanoid Robot Innovation Center has seen global downloads of its open-source dataset RoboMIND surpass 20 million times.

At the recently concluded 9th China Robot Summit, a framework for evaluating robot embodied intelligence capabilities was officially released, establishing a unified measurement standard for the industry. Experts attending the summit unanimously agreed that 2026 has become the "year of mass production" for humanoid robots. As robots log 3,000 continuous hours on production lines and Hangzhou robot school graduates step into their respective workplaces, these scenarios collectively depict an industry transitioning from "capable" to "well-executed." As Tian Feng stated, China is moving in the right direction, though overcoming current challenges will require time.