Frost & Sullivan executives were invited to attend Look·Entrepreneur AI Lecture: From delivery to operation, the commercialization of humanoid robots relies on 'double closed loop' strategy
Frost & Sullivan executives were invited to attend Look·Entrepreneur AI Lecture: From delivery to operation, the commercialization of humanoid robots relies on 'double closed loop' strategy
Viewpoint · Entrepreneurs AI Lecture Hall
On October 14, 2026, during the 2026 China International Industry Expo,Viewpoint · Entrepreneurs AI Lecture Hall — Special Session on Innovation and Development of Embodied Intelligence and Robot Core Componentswas held at the National Exhibition Center in Shanghai.Wang Chenhui, Managing Partner and President of Frost & Sullivan Frost & Sullivan China, was invited to attend and delivered a keynote speechsharing insights on topics such as the commercialization process of humanoid robots, the evolution of embodied intelligence technology, the localization of core components, and the implementation of production scenarios.
From exhibition booths to actual work positions, system capabilities become the focus of competition
Wang Chenhui stated that the most significant change in the humanoid robot industry this year is "moving from exhibition booths to actual work positions." Humanoid robots are no longer just prototype models in laboratories or performance units at exhibitions; they are beginning to exhibit the real form of "job-oriented robots." Scientific research, education, and entertainment demonstrations are the leading scenarios, while productive scenarios such as industrial manufacturing, logistics, and commercial services are becoming the focus of commercialization.
"In the next stage, 'system capabilities' will replace 'single hardware components' as the focus of competition." Wang Chenhui noted that the industry bottleneck has shifted from hardware manufacturing to scenario adaptation and embodied intelligence software capabilities. Many companies can produce prototypes, but only those capable of "managing scenario operations" have long-term competitiveness. Additionally, the development of the "brain" of embodied intelligence is taking over the hardware role. Visual language-action models and multi-modal perception integration are driving robots from "action demonstration" to "continuous complex operations."
From scientific research and education to production operations, commercialization enters the "Fourth Scenario"
Wang Chenhui explained thatthe scenario evolution path of humanoid robots is from "scientific research verification" to "value realization."
Early markets were supported by scientific research, education, and entertainment demonstrations, with customers being universities, laboratories, and event companies, seeking openness and "visibility." Later, data collection scenarios emerged, and embodied intelligence companies paid for model training. Now, production operations have taken over—customers in the automotive, 3C electronics, new energy, and logistics industries pay for tasks such as handling, loading, sorting, and assembly.
He said that the key indicator of this stage is "customers voting through procurement, leasing, or service contracts." The evaluation focuses not on technical parameters, but on operation speed, stability, the need for manual intervention, and return on investment. Production tasks are integrated into the company's daily processes; after completing individual verifications, robots can be replicated to similar workstations, production lines, and operational nodes—a scale characteristic not present in the previous three scenarios.
From complete machines to mass production, core components face systematic upgrades
From the perspective of complete machine manufacturers moving towards mass production, Wang Chenhui believes that requirements for core components are evolving from "being able to produce" to "able to be mass-produced, consistent, and cost-effective."
First is the adaptation of production processes.Many components such as reducers, joint modules, dexterous hands, and sensors are still in small-scale production. Complete machine manufacturers need the supply chain to provide manufacturing solutions for large-scale production.
Second is standardization and consistency.Standardized components and mature manufacturing processes directly determine product consistency and the ability to deliver to mass quantities. Therefore, the localization of core components becomes a key factor in industrialization.
Third is collaborative innovation.Some core components still have performance gaps. Suppliers need to establish a joint definition relationship with complete machine manufacturers, rather than simply a supply-demand relationship, to truly achieve "scenario-defined technology, demand-driven innovation."
From delivery to operation, long-term competitiveness depends on dual closed loops
Wang Chenhui believes that the most critical aspect for humanoid robot manufacturers in the next stage is to move from "delivery capability" to "operational capability," completing both data and business closed loops.
Currently, the industry's delivery scale continues to expand. Global sales in the first half of 2026 exceeded those of 2025, but high growth in shipments does not mean a mature business model. Long-term competitiveness lies in whether reusable data can be continuously generated in real scenarios—scientific research and education accumulate algorithm verifications, data collection accumulates training resources, and production operations accumulate long-tail scenarios, feeding back into model iteration to create a positive feedback loop of "the more deployed, the more data; the more data, the stronger the model." Meanwhile, the ability to deliver on a large scale is still underestimated. From "pilot success" to "multi-scenario replication," standardized products, delivery, and service systems are required.
From scientific research and education to production operations, from core components to the "brain" of embodied intelligence, the industry is using real operational data as the foundation and productive scenarios as the entry point to drive humanoid robots from "moving" to "performing," from "verification" to "deployment."
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