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Insight into the 2025 Blue Book on Humanoid Robotics Industry Development (8)

In the process of promoting the evolution of humanoid robots to higher intelligence, in addition to the existing technical problems, the limitations of data collection methods also pose serious constraints to their "learning" and "adaptation" capabilities. From the perspective of the underlying algorithm architecture of the robot, it is often divided into two parts: "brain" and "cerebellum": the former is responsible for perception, decision-making and cognition, and the latter focuses on movement and action control. However, compared with the rapid progress of the brain in recent years, the progress of humanoid robots in the "cerebellum", that is, motor control, is relatively lagging behind, and one of the key factors causing this gap is the bottleneck of data collection methods.

Limitations of data collection methods

At present, there are two main ways to collect data for cerebellar training:

1.                Video-based learning guidance: Imitation learning by observing live action videos. The disadvantage of this method is that its information representation ability is limited, it is difficult to restore the details of the action realistically, and it cannot completely replace the real interactive experience.

2.                Generative simulation training: A virtual training environment is generated through the physics engine for action evolution and model optimization. Although it can cover a wider range of action scenarios, the robustness and generalization ability of the model are still insufficient in dealing with complex and changeable situations in the real world, and the training quality is highly dependent on the accuracy of physical modeling.

The limitations of these two methods make humanoid robots unable to adapt to changing environments and complex tasks, which has become a difficulty in large-scale deployment and application.

Efficiency challenges caused by the inconsistency of the underlying algorithms

Another constraint is the fragmentation at the algorithmic level. At present, in fine-grained operation scenarios, different tasks often need to set their own independent reward functions to drive reinforcement learning, and it is difficult to build a general underlying control model. As a result, each specific task needs to be developed and tuned separately, resulting in a lack of compatibility and collaboration between systems, which slows down the performance and operational efficiency of humanoid robots as a whole.

Cost challenges: Imports are important for key components, and overall manufacturing costs are high

At the hardware level, the planetary roller screw, which is extremely difficult to manufacture, is one of the key driving components of the current humanoid robot, but the technology is still in its infancy in China, and the overall scale of domestic manufacturers is small, the technical barriers are high, and the production capacity is mainly concentrated in Europe and the United States and other regions, resulting in most high-end screw products still need to rely on imports and are expensive. According to the data, the share of domestic manufacturers in the domestic market is only 19%, which seriously restricts the cost control of the robot machine.

Not only that, the current mainstream humanoid robot products such as Honda, Boston Dynamics, NASA and General Motors jointly developed version, their single manufacturing cost is still as high as more than 2 million US dollars. Even Tesla CEO Elon Musk has publicly stated that only when the price of a single machine drops to the range of $2 to $30,000 can humanoid robots truly achieve large-scale mass production. The reality shows that there is still a big gap between this goal and the current goal.

Figure: Market share of China's planetary roller screws (unit: %)

Figure: Market share of China's planetary roller screws (unit: %)

Cost challenges: Post-maintenance expenses cannot be ignored

In addition to the high investment in early manufacturing, the later operation and maintenance costs of humanoid robots should not be underestimated. The maintenance covers the periodic inspection of mechanical and electrical systems to control modules, the replacement of core components (such as sensors and roller screws, etc.), equipment repair and cleaning maintenance, etc.

For example, the replacement of key components is not only frequent, but also very expensive because most of them rely on high-end products from overseas. In addition, in order to ensure long-term stable operation, enterprises must establish a systematic maintenance plan, which increases the overall cost of the whole life cycle of equipment.

Application bottleneck: It is difficult to meet the requirements of diverse scenarios

At the practical application level, humanoid robots are currently mostly concentrated in simple scenarios with a high degree of standardization, closed environment, and clear operation process. Due to the fact that its interaction ability, environmental perception and human-machine collaboration level still need to be improved, its adaptability in complex and changeable scenarios is still insufficient, and it cannot respond to sudden changes flexibly like humans. This limits its penetration into a wider range of industries and complex processes.

Safety and Ethics: Anthropomorphism Presents New Challenges

As humanoid robots continue to approach human appearance and behavior, the ethical and safety issues brought by them are also attracting increasing attention. At present, the industry has not yet formed a unified safety norms and ethical standards, and the main risks are reflected in the following aspects:

1.                Cognitive error caused by the mismatch between appearance and function: the appearance simulation is high but the function does not meet expectations, which is easy to mislead users to form unrealistic expectations, and even feel "deceived".

2.                Privacy leakage risk: Humanoid robots have strong data collection and dissemination capabilities, and combined with the opacity of algorithms, they may leak sensitive information without the user's authorization.

3.                The "Uncanny Valley" effect: When the robot looks close to a real human but is slightly "weird", it will cause fear and rejection among users.

4.                Emotional confusion and virtual dependence: If abused, emotionally accompanied humanoid robots may cause users to have the illusion of projecting emotions about real people onto the robot, resulting in psychological cognitive biases, especially for children and people with weak cognition.

In summary, humanoid robots face a series of systemic challenges, from data collection and algorithm architecture, to manufacturing costs, application limitations, to safety ethics. The solution of these problems requires not only breakthroughs in software and hardware technologies, but also the improvement of the standard system and in-depth discussion of the ethics of emerging technologies in the whole society. Only in this way can humanoid robots truly take a key step towards large-scale landing.


Related:

Insight into the 2025 Blue Book on Humanoid Robotics Industry Development (1)

Insight into the 2025 Blue Book on Humanoid Robotics Industry Development (2)

Insight into the 2025 Blue Book on Humanoid Robotics Industry Development (3)

Insight into the 2025 Blue Book on Humanoid Robotics Industry Development (4)

Insight into The 2025 Blue Book on Humanoid Robotic Industry Development (5)

Insight into The 2025 Blue Book on Humanoid Robotic Industry Development (6)

Insight into The 2025 Blue Book on Humanoid Robotic Industry Development (7)

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