In the development of humanoid robots, the dexterous hand can be called an insurmountable threshold. Elon Musk and Tesla have repeatedly emphasized its difficulty and value, and the market is also paying close attention to breakthroughs in hand movements. At present, manufacturers of humanoid robot complete machines have made remarkable progress in the motion control of body joints, being able to perform smooth movements such as dancing and boxing, yet the realization of hand movements remains slow. This precisely highlights that the technical difficulty of dexterous hands far exceeds that of body joints, and their importance as the most critical part of humanoid robots has become increasingly prominent.
Core Technical Bottlenecks of Dexterous Hands
The R&D challenges of dexterous hands focus on two major dimensions: hardware and software. In terms of hardware, the primary difficulty lies in high spatial integration. For example, Tesla’s Optimus 2.5 solution needs to accommodate components such as motors and gearboxes required for more than 20 degrees of freedom in an extremely small space, while meeting multiple requirements such as high power density and high precision. Latest robotics news has also highlighted that the second hardware challenge is multi-modal perception fusion, especially tactile sensors, which not only need to ensure data consistency and no performance drift, but also overcome the inherent differences between different perceptual information. On the software front, the algorithm architecture has not yet converged; key issues such as the functional division and deployment location of the “brain” and “cerebellum” lack standard solutions, and dexterous hand control has extremely high requirements for multi-modal information fusion capabilities. Insufficient data volume is the biggest bottleneck: the collection and annotation of human movement data are complex and costly, the amount of data required for humanoid robots far exceeds that for autonomous driving, and simulation training is difficult to reproduce long-tail scenarios in the real world.
Hardware Industry Chain and Industrial Outlook
In terms of hardware technical routes, the “motor + planetary gearbox + micro lead screw + tendon cable” structure of Tesla Optimus has a lighthouse effect. Core hardware includes motors that provide power, planetary gearboxes for speed reduction and torque increase, micro lead screws that realize motion conversion, tendon cables that transmit pulling force, and tactile sensors with an ever-expanding coverage range. In the related industrial chain, Nanshan Zhishang and Tongyizhong have made progress in the field of tendon cable materials, Xinjian Transmission and Zhejiang Rongtai are advancing cooperation with Tesla in the hand assembly segment, and traditional suppliers such as Tuopu Group and Sanhua Intelligent Controls also have great potential.
From the perspective of industry development, dexterous hands are an inevitable choice for humanoid robots. In terms of functions, they are a universal medium connecting thousands of tools, and the core for humanoid robots to achieve task generalization and environmental adaptation. In terms of data accumulation, their movements can be directly mapped to humans, greatly improving the efficiency of model training. However, the commercialization process will proceed step by step: initially, it will focus on limited scenarios such as data collection in Tesla factories and scientific research education, and simplified forms such as two-finger and three-finger hands may take the lead in commercial application.
At present, the dexterous hand industry still faces risks such as unconverged technical routes and supply chains. Cost reduction, improvement of perception accuracy, and material upgrading remain key iterative directions. With technological breakthroughs and data accumulation, dexterous hands will gradually break through bottlenecks and become the core support for humanoid robots to move towards generalization and commercialization.
