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世界机器人大会|交卷时刻:人形机器人价格战尚早
Bei Jing Shang Bao· 2025-08-10 14:30
Core Insights - The robot industry is experiencing significant growth, with a 27.8% year-on-year revenue increase in the first half of 2025, indicating a shift from technology showcase to mass production [1] - The price of humanoid robots is expected to decrease significantly, as evidenced by the launch of the R1 model at a starting price of 39,900 yuan [1][7] - Companies are focusing on practical applications and commercial viability, with a notable shift towards the cultural and tourism sectors, which now account for 60% of revenue for some firms [3][4] Industry Growth - The humanoid robot industry is projected to double its shipment volume annually in the coming years [6] - Companies like Yunmu Zhizao have seen a dramatic increase in revenue, from over 4 million yuan in 2024 to more than 55 million yuan in 2025, with expectations to exceed 100 million yuan for the year [3] Market Dynamics - The competition is intensifying, with companies needing to answer whether their orders can outpace technological advancements and if they are prepared for price competition [1] - The emergence of lower-priced humanoid robots, such as the R1, raises questions about potential price wars in the industry, although some industry leaders believe that price is not the primary constraint [7][8] Application and Commercialization - The commercial application of bipedal humanoid robots in industrial settings is still in its early stages, with most deployments being limited to specific job functions [4][10] - Companies like Qingtong Intelligent have deployed over 100,000 robotic products, with significant growth in overseas markets [5] Technological Challenges - The industry faces challenges related to the lack of operational data for industrial applications, which is crucial for developing effective models for humanoid robots [15] - The complexity of creating high-quality embodied intelligence is a significant barrier to mass production, requiring advancements in data collection and model training [15][16]