In the context of manufacturing digital transformation, I have observed that humanoid robots, as deep integrations of artificial intelligence and mechanical engineering, are reshaping production models through technological breakthroughs and scenario applications. Manufacturing serves as a critical pillar of the national economy and a vital tool for industrial upgrading. Currently, manufacturing is undergoing profound changes, where digital transformation is not only a key pathway to enhance enterprise competitiveness and achieve sustainable development goals but also a core measure to advance new industrialization and build a modern industrial system. Intelligent equipment, production lines, workshops, and factories are widely adopted in enterprises, demonstrating how information technology, automation, and AI empower traditional manufacturing, from improving quality and efficiency to reducing energy consumption and carbon emissions. In this process, humanoid robots, as comprehensive embodiments of these cutting-edge technologies, are injecting new momentum into manufacturing transformation and upgrading.
Humanoid robots integrate innovations from multiple fields, including artificial intelligence, mechanical engineering, and electronics, featuring human-like appearances, flexible mobility, intelligent perception, autonomous decision-making, and efficient interaction. They deeply embed into complex production processes, with applications expanding from automotive manufacturing to 3C electronics, logistics warehousing, and precision instrument production. These humanoid robots excel in high-intensity, high-precision tasks, reducing labor costs while enhancing production stability and product quality. From my perspective, the rise of humanoid robots marks a significant shift toward more adaptive and intelligent automation in industries.
Breakthroughs in Key Technologies of Humanoid Robots
Since 2021, policies such as the “14th Five-Year Plan for Robot Industry Development” and the “Guiding Opinions on Innovative Development of Humanoid Robots” have been implemented, aiming to establish an innovation system by 2025 with breakthroughs in key technologies like the “brain, cerebellum, and limbs,” ensuring secure and effective supply of core components. By 2025, humanoid robots have achieved remarkable progress, transitioning from laboratory research to commercial applications and laying a solid foundation for large-scale industrialization. I have categorized these breakthroughs into three main areas, supported by data and analyses.
First, in motion capabilities, humanoid robots have significantly improved their adaptability to complex terrains and enhanced their agility and precision. For instance, the “Tiangong” robot developed by the Beijing Humanoid Robot Innovation Center can run at speeds up to 12 km/h on varied terrains like sand, snow, and slopes, and it has successfully climbed 134 consecutive outdoor stairs, reaching the highest point of Haiziqiang Park in Tongzhou District. This represents a global milestone for humanoid robots operating in challenging environments. The motion dynamics can be modeled using equations of motion, such as: $$ \vec{F} = m \vec{a} $$ where \(\vec{F}\) is the force applied, \(m\) is the mass, and \(\vec{a}\) is the acceleration. Additionally, the stability of humanoid robots on uneven surfaces can be analyzed through the zero-moment point (ZMP) criterion: $$ \text{ZMP} = \frac{\sum m_i (g \times r_i)}{\sum m_i g} $$ where \(m_i\) is the mass of each segment, \(g\) is gravity, and \(r_i\) is the position vector.
| Parameter | Initial Performance | Current Performance | Improvement |
|---|---|---|---|
| Running Speed (km/h) | 6 | 12 | 100% |
| Terrain Types | Limited flat surfaces | Sand, snow, slopes, stairs | Multiple complex environments |
| Climbing Ability | Basic steps | 134 consecutive stairs | Significant endurance |
Second, in intelligent control, the “brain” of humanoid robots has seen enhanced performance, with the “cerebellum” achieving more precise control. The brain, as the central decision-making system, combines high-performance computing platforms and advanced algorithms to process complex sensor data streams, enabling accurate environmental perception and high-level decision-making. The cerebellum translates decisions into specific motion commands, ensuring coordination and accuracy. Control methods are evolving from model-based to learning-based approaches, such as reinforcement learning: $$ Q(s,a) = Q(s,a) + \alpha [r + \gamma \max_{a’} Q(s’,a’) – Q(s,a)] $$ where \(Q(s,a)\) is the action-value function, \(\alpha\) is the learning rate, \(r\) is the reward, and \(\gamma\) is the discount factor. This allows humanoid robots to adapt dynamically to unpredictable manufacturing scenarios.
Third, in energy management, advancements in battery and fast-charging technologies have supported efficient power usage. For example, GAC Group’s third-generation embodied intelligent humanoid robot, GoMate, equipped with solid-state batteries, achieves up to 6 hours of endurance with over 80% energy savings compared to similar products. High-performance lithium batteries are commonly used, with ongoing improvements in energy density. Fast-charging technologies are becoming crucial, enabling humanoid robots to recharge quickly and minimize downtime. The energy efficiency can be expressed as: $$ \eta = \frac{P_{\text{output}}}{P_{\text{input}}} \times 100\% $$ where \(\eta\) is efficiency, \(P_{\text{output}}\) is useful power output, and \(P_{\text{input}}\) is total power input. A table below summarizes energy-related parameters.
| Aspect | Technology | Performance | Impact |
|---|---|---|---|
| Battery Type | Lithium-based | High energy density | Extended operation |
| Endurance | Solid-state batteries | Up to 6 hours | Reduced interruptions |
| Energy Savings | Optimized algorithms | 80% improvement | Lower costs |
| Charging Time | Fast-charging systems | Minutes for high recharge | Enhanced productivity |
Demands for Humanoid Robots in Manufacturing Digital Transformation
Government work reports emphasize accelerating manufacturing digital transformation by cultivating service providers proficient in both industry and digital technologies and supporting small and medium-sized enterprises in their transitions. The integration of “intelligent and green” digital technologies is pivotal for advancing new industrialization. Notably, “embodied intelligence” and “intelligent robots” have been highlighted as key focuses for next-generation smart terminals. From my analysis, manufacturing digital transformation involves deep integration of digital technologies to achieve intelligent, efficient, and flexible production, management, and services, thereby boosting productivity, reducing costs, optimizing resources, and meeting personalized demands. Humanoid robots, as products of AI and robotics fusion, provide a physical form for AI, enabling practical applications in operations.
Manufacturing digital transformation requires deep empowerment from humanoid robots in several areas. First, to improve production efficiency, humanoid robots handle repetitive tasks and optimize processes. By integrating with MES/ERP systems, they enable real-time data collection and dynamic scheduling, enhancing efficiency in assembly and handling by 3–5 times compared to manual labor. The closed-loop feedback mechanism allows precise control of production rhythms, shortening product cycles by over 30% and reducing work-in-progress inventory by 20–35%. The productivity gain can be modeled as: $$ \text{Efficiency Ratio} = \frac{T_{\text{manual}}}{T_{\text{robot}}} $$ where \(T_{\text{manual}}\) is time taken manually and \(T_{\text{robot}}\) is time with humanoid robots. If \(T_{\text{robot}} = \frac{1}{4} T_{\text{manual}}\), then the ratio is 4, indicating a quadrupling of efficiency.
Second, in cost reduction, humanoid robots replace part of the human workforce and minimize resource consumption. Amid rising labor costs, these robots offer high precision (error ≤ 0.03 mm) and stable performance (operating 24/7), taking over high-load, high-precision tasks. Through intelligent torque control systems, they optimize operational parameters, increasing raw material utilization by 15–20% and reducing energy consumption by 8–12%. According to McKinsey research, enterprises adopting humanoid robot solutions can lower comprehensive production costs by 18–25%, with a return on investment period of 2–3 years. This addresses labor shortages and builds sustainable advantages through refined resource management. The cost-saving formula is: $$ C_{\text{savings}} = C_{\text{baseline}} – C_{\text{robot}} $$ where \(C_{\text{baseline}}\) is baseline cost and \(C_{\text{robot}}\) is cost with humanoid robots.
| Metric | Traditional Manual | With Humanoid Robots | Improvement |
|---|---|---|---|
| Production Efficiency | Baseline | 3–5x increase | Significant time savings |
| Cost Reduction | High labor costs | 18–25% lower | Substantial savings |
| Error Rate | Variable | ≤ 0.03 mm | High precision |
| Inventory Reduction | Standard levels | 20–35% decrease | Better resource use |
Third, in human-robot collaboration and safety, upgrades in natural language processing and deep learning enable humanoid robots to accurately interpret voice commands, engage in multi-turn dialogues, and collaborate with workers on complex tasks, improving efficiency and quality. Additionally, in hazardous manufacturing environments, humanoid robots equipped with real-time monitoring, dynamic obstacle avoidance, and emergency braking systems enhance safety by replacing humans in dangerous conditions, significantly reducing accident probabilities. The safety improvement can be quantified as: $$ P_{\text{accident}} = P_{\text{baseline}} \times (1 – \eta_{\text{robot}}) $$ where \(P_{\text{accident}}\) is the probability of accidents with humanoid robots, \(P_{\text{baseline}}\) is the baseline probability, and \(\eta_{\text{robot}}\) is the risk reduction factor (e.g., 0.5 for 50% reduction).
Fourth, to adapt to variable production demands, humanoid robots flexibly adjust capabilities and meet customization needs. As market demands shift rapidly, these robots can quickly adapt to different tasks and environments through software upgrades and reprogramming, enabling rapid reconfiguration of production lines for small-batch, multi-variety production. They adjust parameters and operations based on order requirements, facilitating personalized product customization. The flexibility can be expressed using a adaptability index: $$ A = \frac{N_{\text{configurations}}}{T_{\text{setup}}} $$ where \(A\) is adaptability, \(N_{\text{configurations}}\) is the number of possible configurations, and \(T_{\text{setup}}\) is setup time. Higher \(A\) values indicate better responsiveness to changes.
Technical Advantages of Humanoid Robots in Manufacturing Digital Transformation
While traditional industrial robots dominate current manufacturing, with rising automation and intelligence enabling more precise operations and higher efficiency, they are often fixed in place, designed for specific processes, and lack mobility and flexibility. For example, in bottling lines, changing bottle types may require redesigning grippers, and they typically operate in isolation with limited safety measures for human collaboration. In contrast, humanoid robots overcome these limitations, offering superior technical advantages that align with modern manufacturing needs for adaptability and cooperation.
Humanoid robots exhibit human-like forms and flexibility, with multi-degree-of-freedom joint structures that allow multi-directional extension, rotation, and bending of arms for tasks like rapid grasping, precise placement, and assembly. In complex environments, this flexibility enables easy adaptation to various tasks. For instance, in automotive manufacturing, humanoid robots navigate narrow spaces to install and debug components, while in electronics, they adjust postures swiftly to handle unexpected situations, ensuring continuous and stable production. The kinematic model for a humanoid robot arm can be described using Denavit-Hartenberg parameters: $$ T_i^{i-1} = \begin{bmatrix} \cos\theta_i & -\sin\theta_i \cos\alpha_i & \sin\theta_i \sin\alpha_i & a_i \cos\theta_i \\ \sin\theta_i & \cos\theta_i \cos\alpha_i & -\cos\theta_i \sin\alpha_i & a_i \sin\theta_i \\ 0 & \sin\alpha_i & \cos\alpha_i & d_i \\ 0 & 0 & 0 & 1 \end{bmatrix} $$ where \(T_i^{i-1}\) is the transformation matrix between links, \(\theta_i\) is the joint angle, \(\alpha_i\) is the twist angle, \(a_i\) is the link length, and \(d_i\) is the link offset.
| Feature | Traditional Industrial Robots | Humanoid Robots | Advantage of Humanoid Robots |
|---|---|---|---|
| Mobility | Fixed position | Mobile and adaptable | Better for dynamic environments |
| Flexibility | Limited to specific tasks | Multi-task capable | Reduces redesign needs |
| Human Collaboration | Minimal safety features | Advanced safety and interaction | Enhances teamwork |
| Intelligence | Basic programming | AI-driven perception and decision | Higher autonomy |
Humanoid robots possess intelligent perception and decision-making capabilities, equipped with advanced computer vision systems for precise environmental perception, such as part recognition and quality inspection. In machining workshops, they detect defects like scratches or cracks on components. Natural language processing and deep learning enable them to understand and execute worker instructions via voice or text, and they learn from production data to make informed decisions, such as predicting equipment failures for preventive maintenance or adjusting production plans based on progress and inventory. The perception accuracy can be modeled as: $$ \text{Accuracy} = \frac{\text{True Positives} + \text{True Negatives}}{\text{Total Samples}} $$ and for decision-making, a Bayesian approach can be used: $$ P(H|E) = \frac{P(E|H) P(H)}{P(E)} $$ where \(P(H|E)\) is the posterior probability of hypothesis \(H\) given evidence \(E\).
Human-robot collaboration is another key advantage, as humanoid robots work synergistically with human workers to form efficient teams. In tasks requiring fine motor skills, such as electronic chip assembly, humans focus on creative and complex judgments like design optimization, while humanoid robots handle high-precision placement and welding. Safety designs include force feedback systems that detect collisions and halt movements promptly, and ergonomic designs avoid sharp edges to minimize injury risks. Interactive features, such as displays showing work status and voice prompts, facilitate communication, allowing workers to monitor robot activities and collaborate effectively. The collaboration efficiency can be expressed as: $$ \text{Collaboration Score} = \frac{\text{Joint Task Completion Rate}}{\text{Individual Task Time}} $$ higher scores indicate better synergy.
Challenges and Future Prospects
As frontier products of AI and robotics integration, humanoid robots show vast application prospects in manufacturing digital transformation. They enhance production efficiency and product quality by optimizing processes, improving quality inspections, providing data analysis support, and strengthening human-robot collaboration. Moreover, they support intelligent management in enterprises. However, humanoid robots do not entirely replace humans; instead, they take over repetitive, labor-intensive, or hazardous tasks. With industrial-scale development, humanoid robots can stimulate growth across the supply chain, from components to整机, creating new job opportunities and employment.
Despite the promising outlook, humanoid robots face challenges, including high technical barriers, rapid technological updates, complexity, high costs, and insufficient adaptation to existing workflows. These factors must be considered when promoting their adoption. Nevertheless, as产业链成熟s and large-scale applications materialize, humanoid robots are poised to become core productivity drivers in future smart factories, injecting new momentum into high-quality manufacturing development. The future growth can be projected using a logistic function: $$ N(t) = \frac{K}{1 + e^{-r(t-t_0)}} $$ where \(N(t)\) is the number of humanoid robots deployed at time \(t\), \(K\) is the carrying capacity, \(r\) is the growth rate, and \(t_0\) is the inflection point.
In the future, with policy support, technological innovation, and industrial collaboration, humanoid robots will find applications in broader industrial scenarios, accelerating the global manufacturing digital transformation process. I believe that continuous advancements will overcome current limitations, making humanoid robots indispensable in achieving sustainable and intelligent manufacturing ecosystems. The integration of humanoid robots will not only drive economic benefits but also foster a safer and more innovative working environment, ultimately contributing to long-term industrial resilience and growth.