Current Landscape and Development Trends of Humanoid Robots
Han Ding, An Li
- 发表年份
- 2025
- 引用次数
- 3
- 访问权限
- 开放获取
摘要
Humanoid robots are advanced general-purpose machines that mimic the human body, motion, and intelligence. They are capable of seamlessly integrating into human environments and utilizing existing infrastructure. These robots can assist or replace humans in hazardous and complex tasks across a variety of fields, including national defense and security, industrial manufacturing, aerospace exploration, and social services. Typically, humanoid robots feature human-like structures, such as heads, torsos, and limbs, and possess capabilities, such as perception and decision-making, motion control, and limb actuation. Equipped with advanced sensors, they can capture visual, tactile, and auditory information, simulate human neural transmission through control systems, replicate human joint movements via servo motors, and perform dexterous tasks with robotic hands. Since the 1960s, humanoid robot technology has made a major leap from theoretical exploration to practical application. Today, the field is undergoing a golden era of development. Breakthroughs in key technologies and accelerating industrialization have painted a promising future for widespread deployment of humanoid robots—especially in critical sectors such as manufacturing, healthcare (companionship and care), and specialized tasks (such as power grid maintenance). However, most of the impressive achievements in humanoid robots to date remain breakthroughs at specific points rather than comprehensive capabilities with real societal impact. Although various humanoid robot prototypes have been released internationally, several bottlenecks still hinder their industrial application: First, they fail to meet the requirements for stable motion and long-endurance performance. Second, their perception remains primarily unimodal, limiting their ability to perform dexterous operations. Third, their long-sequence processing capabilities are only about one-tenth those of humans, falling short of the requirements for long-sequence decision-making. Hence, there is an urgent need to overcome three core challenges—long-endurance stable locomotion, dexterous manipulation capabilities, and embodied intelligence—to lay a solid foundation for core technological innovation and the industrial development of humanoid robots. Only by truly grounding the technology in real-world scenarios and continuously advancing productization, we can achieve the leap from “watchable” to “usable” humanoid robots. Humanoid robots represent the convergence and breakthroughs of multiple disciplines, including mechanical engineering, control engineering, intelligent science, and materials engineering. They are a culmination of robotics technology and a vital window into the exploration of future intelligent life. Their development toward general-purpose and intelligent capabilities hinges on the deep integration of cutting-edge science and advanced technologies. Overall, breakthroughs are still needed in the development of humanoid robots in the following areas: (1) Deep integration of bionics and mechanical engineering. By applying principles of bionics to imitate the movement mechanisms of biological organisms and replicating human body structures and motion patterns, mechanical structures resembling the bones, joints, muscles, and skin of humans can be created. Combined with new findings in mechanical engineering, this enables the precise selection of materials and the design of hybrid serial–parallel structures that achieve both rotational and linear motions. This fusion not only enhances the motion flexibility of humanoid robots but also improves their energy efficiency, bringing them closer to human-like appearance and behavior, and paving the way for breakthroughs in efficiency and endurance. (2) Breakthroughs in sensor technology and control theory. A humanoid robot's ability to perceive the environment through multiple modalities is key to its intelligence. Although visual perception is especially critica
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991