At the recent “2025 Manufacturing Power Construction Forum,” a roundtable discussion centered on the theme “Mass Production Challenges and Ecosystem Co-creation of Embodied Intelligence” brought together industry leaders and experts to explore the critical hurdles and collaborative strategies needed to advance embodied intelligence technologies. Embodied intelligence, which integrates artificial intelligence with physical forms like robots to enable adaptive, interactive behaviors, is increasingly recognized as a foundational technology for high-level intelligent manufacturing. It empowers diverse applications, including production processes, warehousing and logistics, inspection and maintenance, and human-machine collaboration. However, the transition from laboratory innovations to large-scale production of embodied intelligence systems faces significant barriers, as highlighted by forum participants.
-
Why Mass Production Remains Elusive for Embodied Intelligence
The mass production of embodied intelligence is hampered by several interconnected challenges, starting with the absence of a unified underlying technical architecture. Wang Shuo, a member of the National Manufacturing Power Construction Strategy Advisory Committee and a researcher at the Institute of Automation, Chinese Academy of Sciences, emphasized that the embodied intelligence industry confronts a critical issue: the underlying technical frameworks have not yet converged into a standardized form. This lack of convergence means that different forms of equipment integrated with intelligence lack a common technical foundation, leading to compatibility issues and inefficiencies in scaling. For instance, the proposal from Anhui for a universal technical base for robots represents a forward-thinking approach—by establishing a unified framework, it aims to resolve adaptation problems across multi-form robots and provide core support for industrial scaling. Future efforts must prioritize the convergence of technical architectures to develop reusable standard systems, facilitating a shift from isolated innovations to ecosystem-wide advancements in embodied intelligence.
Another major obstacle is the divergence in technical routes. Xu Jiabin, a member of the National Manufacturing Power Construction Strategy Advisory Committee and a professor at the School of Business, Renmin University of China, pointed out that embodied intelligence technology is currently in an explosive growth phase, but the proliferation of competing technical pathways creates a “thousand boats competing” scenario that hinders mass production. Without a dominant, unified technical route, manufacturers face difficulties in achieving economies of scale, as varied approaches lead to fragmented development and interoperability issues. This diversity, while fostering innovation, complicates the standardization necessary for efficient production lines and widespread adoption of embodied intelligence solutions.
High-quality data acquisition poses a core challenge for training embodied intelligence models. Chen Jihong, director of the National Numerical Control System Engineering Technology Research Center and chairman of Wuhan Huazhong Numerical Control Co., Ltd., explained that enterprise-specific process data, which represents valuable knowledge assets accumulated on production lines, is often treated as proprietary and confidential. This results in data silos that prevent the formation of public datasets, directly impairing the training efficacy of intelligent models. Without access to rich, high-quality process data, numerical control systems and other embodied intelligence applications cannot undergo continuous optimization, leading to products that fall short of user expectations and creating a vicious cycle where technological upgrades fail to yield commercial returns. The tension between knowledge protection and model training is particularly acute; companies must leverage the generalization capabilities of large models while safeguarding sensitive data from exposure during training processes.
To address this, Chen Jihong described a hybrid architecture combining fine-tuning with knowledge graphs, which seeks to balance technological openness with data confidentiality. By utilizing open-source base models to reduce training costs and encapsulating enterprise-specific data within localized knowledge graphs, this approach enables a closed-loop training system where data remains within the factory premises. This strategy mitigates the risks associated with public model exposure and alleviates the financial burden on companies that would otherwise need to undertake independent large-model training for embodied intelligence applications.
Wang Shuo further elaborated that embodied intelligent agents exhibit significant variations in multi-dimensional requirements, such as force, position information, tactile sensing, and infrared perception. These differences necessitate dynamic data screening tailored to specific environments to support precise decision-making. Moreover, a deeper challenge lies in achieving generalization capabilities—enabling embodied intelligence systems to transcend single-scene limitations and apply learned knowledge across diverse contexts through transfer learning. This ability to “draw inferences from one instance” is not only a technical bottleneck but also a crucial factor in moving embodied intelligence from controlled laboratory settings to real-world, scalable applications.
The lack of established standards further impedes mass production efforts. Xu Jiabin stressed that industrialization and scaling depend on the establishment of pioneering and leadership-oriented technical standards; without them, effective quantification and consistency in production become unattainable. The chosen technical route will directly influence the pace at which embodied intelligent agents and the broader artificial intelligence industry can achieve规模化, underscoring the urgency of standardizing protocols and metrics.
Zhai Yanqi, vice president of Shanghai Fourier Intelligent Technology Co., Ltd., provided a practical perspective, noting that while debugging a single embodied robot is manageable, scaling to thousands or tens of thousands introduces immense complexity. Currently, leading companies in embodied intelligence have achieved small-scale production, ranging from tens to hundreds or even thousands of units. However, true mass production, as seen internationally, involves manufacturing on the scale of tens of thousands of robots. Zhai Yanqi identified four primary difficulties in reaching this level: first, ensuring the stability and consistency of technology, where algorithm robustness and motion control stability are hard to maintain across large batches, leading to product variability; second, the absence of socially recognized stability indicators, which blurs the boundaries for product acceptance and quality assurance; third, supply chain vulnerabilities, including “bottleneck” issues with key components and persistently high costs that constrain large-scale production; and fourth, the incomplete formation of a commercial closed loop, requiring collaborative exploration with customers to identify and develop high-value application scenarios for embodied intelligence. Despite these hurdles, Zhai Yanqi expressed optimism that with supportive national policies, the mass production bottleneck for embodied intelligence robots could be overcome within three to five years.
Xiong Meng, a member of the National Manufacturing Power Construction Strategy Advisory Committee and executive vice president and secretary-general of the China Federation of Industrial Economics, emphasized that the integration of new technologies like embodied intelligence into industrial ecosystems requires a complete cycle of development. The eventual爆发 of the embodied intelligence industry hinges on the convergence of underlying technical architectures, the enhancement of infrastructure, and the continual refinement of application scenarios across various sectors. This holistic view acknowledges that mass production is not solely a technical issue but also depends on systemic maturation and collaborative efforts.
-
Technical Routes as the Key to Scaling Embodied Intelligence
The development of effective technical routes is paramount for overcoming the mass production challenges of embodied intelligence. Chen Feng, a researcher at Anhui University Embodied Intelligence Research Institute and founder and chairman of Hefei Zhongke Sheng Valley Technology Development Co., Ltd., outlined a comprehensive technical breakthrough path adopted by his company. This approach begins with high-precision simulation systems that address 60% to 70% of data collection challenges, providing a standardized data foundation for embodied intelligence. By leveraging simulations, companies can generate vast amounts of training data without the constraints of physical environments, accelerating the development of robust models for embodied robots. The next step involves developing agile control technologies that enable millisecond-level responses in robot motion control and facilitate seamless coordination between “brain” and “body” components of embodied intelligence systems. Finally, establishing a cross-modal interaction framework creates an intelligent network that supports dynamic communication between different sensory and execution modules. This vertical integration strategy—spanning from底层 data acquisition to upper-layer交互—forms a technical moat that enhances the perception-decision-execution闭环 in embodied intelligence, making it more adaptable and efficient for real-world applications.
Xu Jiaban reinforced that the本体 of embodied intelligence remains rooted in mechanical equipment, meaning that its advancement is intrinsically linked to the progress of supporting industries such as基础零部件 and工艺制造. A breakthrough in embodied intelligence cannot occur in isolation; it must be built on a foundation of multi-industry collaboration, where innovations in components, manufacturing processes, and integration technologies evolve in tandem. This interdependence highlights the need for synchronized upgrades across the supply chain to ensure that embodied robots can be produced reliably and at scale.
-
Industrial Chain Collaboration as the Foundation for Embodied Intelligence Ecosystem
The successful mass production of embodied intelligence depends heavily on ecosystem co-creation, which involves coordination across multiple points in the value chain. As Zhai Yanqi described, the embodied intelligence industry encompasses a comprehensive system ranging from the most fundamental levels—such as motors and mechanical components—to本体 applications and the practical implementation of application scenarios. This “multi-point” nature means that challenges in one area, like component supply or software integration, can ripple through the entire system, impeding scalability. To address this, Zhai Yanqi advocated for the promotion of standardized systems that systematically verify the generalization and robustness of technologies. By establishing common benchmarks and testing protocols, the industry can ensure that embodied intelligence solutions perform consistently across different environments, thereby supporting large-scale deployment. Additionally, collaborative initiatives among manufacturers, suppliers, and end-users are essential to identify high-value use cases and refine products based on real-world feedback, closing the loop between development and commercialization for embodied intelligence.
Xu Jiabin further elaborated that the embodied intelligence ecosystem requires a synergistic approach where advancements in one sector, such as robotics or AI, are supported by parallel developments in related fields like materials science, electronics, and software engineering. This multi-industry collaboration not only accelerates innovation but also mitigates risks associated with supply chain disruptions and technical incompatibilities. For instance, improvements in motor efficiency or sensor accuracy can directly enhance the performance of embodied robots, while standardized communication protocols can facilitate interoperability between different systems. By fostering an environment of shared knowledge and resources, stakeholders can collectively address the complexities of mass production and unlock the full potential of embodied intelligence in transforming industries such as manufacturing, healthcare, and logistics.
-
Overcoming Data and Generalization Hurdles in Embodied Intelligence
Data-related challenges continue to be a central focus in the quest for mass production of embodied intelligence. The proprietary nature of enterprise data, as highlighted by Chen Jihong, creates significant barriers to developing comprehensive training datasets for embodied intelligence models. Without access to diverse, high-quality data, the ability of embodied robots to learn and adapt in dynamic environments is limited. This issue is compounded by the need for multi-modal data—incorporating visual, tactile, auditory, and other sensory inputs—to enable embodied intelligence systems to perceive and interact with their surroundings effectively. Experts at the forum stressed the importance of developing data-sharing frameworks that respect intellectual property while promoting collaboration, such as federated learning techniques that allow model training without centralizing sensitive data. These approaches can help build more robust embodied intelligence applications by leveraging aggregated insights from multiple sources, ultimately enhancing the reliability and scalability of embodied robots in production settings.
Generalization capability remains another critical hurdle. Wang Shuo’s insights underscored that embodied intelligence must move beyond task-specific functionalities to achieve broad applicability across varied scenarios. This requires advances in transfer learning and adaptive algorithms that allow embodied robots to apply knowledge from one context to another, reducing the need for extensive retraining or customization. For example, an embodied robot trained in a controlled factory environment should be able to operate in unstructured settings, such as outdoor logistics or disaster response, with minimal adjustments. Achieving this level of generalization involves not only technical innovations but also the creation of rich, simulated environments that expose embodied intelligence systems to a wide range of conditions during training. As these technologies mature, they will play a pivotal role in enabling the widespread adoption of embodied intelligence, making it feasible to deploy large numbers of embodied robots across diverse industries without compromising performance or safety.
-
Future Outlook and Policy Implications for Embodied Intelligence Mass Production
The discussions at the forum painted a cautiously optimistic picture for the future of embodied intelligence mass production. While current challenges related to technical convergence, data availability, supply chain stability, and standardization are substantial, the collective expertise and collaborative spirit among industry players offer a pathway to resolution. National policies aimed at promoting innovation and infrastructure development are expected to accelerate progress, as seen in initiatives like Anhui’s universal technical base proposal. Over the next three to five years, breakthroughs in areas such as agile control, cross-modal integration, and ecosystem standardization could pave the way for embodied intelligence to achieve true规模化, transforming it from a niche technology into a mainstream driver of intelligent manufacturing. However, success will depend on sustained investment in research and development, as well as proactive efforts to build inclusive ecosystems that engage all stakeholders—from component suppliers to end-users—in co-creating solutions for embodied intelligence. By addressing these essentials, the industry can unlock the transformative potential of embodied robots, enabling them to perform complex tasks autonomously and collaboratively, and ultimately contributing to broader economic and societal benefits.
In summary, the mass production of embodied intelligence represents a complex yet attainable goal that requires concerted efforts across technical, industrial, and policy domains. The insights from the “2025 Manufacturing Power Construction Forum” highlight the importance of converging technical architectures, unifying routes, enhancing data strategies, and fostering ecosystem collaboration to overcome existing barriers. As embodied intelligence continues to evolve, its integration into smart manufacturing and other sectors will depend on these foundational preparations, ensuring that embodied robots can be produced efficiently, reliably, and at scale to meet the demands of a rapidly advancing technological landscape.