In recent years, Beijing has prioritized the humanoid robot industry as a key driver for developing advanced productive forces. Through a series of systematic policy measures, the city is accelerating the construction of technological innovation and industrial ecosystems for humanoid robots.

In August this year, the Beijing Economic-Technological Development Area Management Committee issued the "Several Measures to Promote the Innovative Development of Embodied Intelligent Robots in Beijing Economic-Technological Development Area." These measures include substantial financial support, such as "up to 5 million yuan per laboratory," "opening data collection scenarios in industrial parks, office buildings, hotels, hospitals, and pharmacies, with selected benchmark embodied intelligent training fields receiving a 100,000 yuan recognition reward each," and "subsidizing enterprises purchasing data products like datasets, data interfaces, and models with 'data vouchers' covering 10% of annual transaction amounts."

These policies not only guide humanoid robot companies but also provide financial backing, fostering industrial chain refinement and commercialization. In October, Beijing Accelerate Evolution Technology Co., Ltd. (Accelerate Evolution) launched its entry-level embodied intelligent development hardware platform, BoosterK1. The first batch of 100 units was sold at a limited-time price of 29,900 yuan, significantly lowering the barrier for developers and reducing commercial application costs.

Cheng Hao, founder of Accelerate Evolution, stated that the development and operational costs of humanoid robots are decreasing, thanks to China's unique supply chain advantages, which are accelerating industrialization and cost reduction—a positive signal for the industry. The company has established a comprehensive competition ecosystem, covering event execution, software/hardware support, brand collaboration, and commercialization.

The industry remains optimistic about the commercialization of humanoid robots. Meng Pengfei, chief machinery analyst at Kaiyuan Securities Research Institute, noted in a recent report: "We believe 2026 will mark the first year of mass production for domestic humanoid robots, characterized by rapid technological breakthroughs, supply chain maturation, and initial commercialization trials. We are bullish on investment opportunities in whole-machine manufacturing, hardware supply chains, and standardization (testing) as large-scale production begins."

The "Humanoid Robot Industry Development Research Report" by the China Academy of Information and Communications Technology (CAICT) proposed a three-phase approach to commercialization. Phase one involves policy-driven exploration of high-value specialized applications, such as hazardous tasks and extreme environments, to facilitate product development and deployment. Phase two focuses on industrial and logistics scenarios, starting with repetitive labor replacement to refine technology, reduce costs, and improve performance. Phase three emphasizes AI integration to achieve advanced embodied intelligence, enabling humanoid robots to serve healthcare, education, retail, and household settings.

Compared to traditional robots, humanoid robots offer distinct advantages, fueling industry optimism about their commercial potential. Wei Yufei, founder and CEO of Beijing Qingfei Technology Co., Ltd., explained that traditional robots are "specialists" (e.g., welding, inspection, sorting robots), while humanoid robots aim to be "generalists." By integrating motion control, environmental perception, and AI models into a unified system, they can perform diverse tasks—like delivering drinks, assisting the elderly, or adjusting curtains—enhancing single-unit value and business scalability. This versatility is key to cost reduction at scale.

However, Wei acknowledged challenges: "Commercialization faces dual tests of cost and scenario adaptation. Standardization and reliability in complex environments remain hurdles. Achieving stable, safe performance outside labs is vastly different from controlled settings. The current reliability and task success rates still fall short of mass commercialization, prompting the industry to seek the optimal balance between human-like intelligence and ultra-stable performance."