"人形机器人要形成生产力，不能满足于娱乐化和遥控操作。" "做机器人不适合'掀桌子式'的创业，一定要在真实场景中迭代，不能只在实验室里埋头苦干。" "没有短期落地，就没有机会、没有资格去谈长期布局。" 8月12日结束的2025世界机器人大会（WRC）上，商业化的迫切性被创业者频频谈起，场景落地已成为 具身智能创业公司的"必答题"。 尽管赛道上存在所谓"运动派"和"实操派"的分野，但这不意味着前者只表演不卖货。从"运动派"公司创 始人的表态来看，他们并非意识不到"干活"才是真谛，也并非不清楚科研教育、导览表演等市场的"天 花板"，更多是对商业化的短期打法存在不同判断。不可否认，主打提供情绪价值的"运动派"，确实从 流量红利中挣到了实实在在的订单。 人形机器人"干活"面临的最大挑战在于，具身智能模型能力不足。这几乎是这条充满非共识赛道上的一 项共识。但对于如何提升模型能力，业内的关注重点显现差异。主流声音认为，数据不足是模型能力难 以泛化的短板。但宇树科技创始人王兴兴则在WRC期间反思称，现在最大的卡点是模型问题而非数据 问题，"具身智能机器人模型架构都不够好"。 创业公司背后的投资机构亦愈发强调商业化能力。首程 ...

Group 1 - The core viewpoint of the articles revolves around the mixed public perception of humanoid robotics, highlighting both the enthusiasm and skepticism surrounding the industry's current capabilities and future prospects [1][2][3] - The term "flower fists and embroidered legs" is used to question the practical significance of current humanoid robot demonstrations, as many companies focus on showcasing their hardware capabilities rather than practical applications [2][4] - Companies like Zhizhong and Yusheng are actively engaging in "show-off" projects, with events planned to demonstrate the limits of their robots, indicating a strategy to build credibility and market presence through entertainment [4][5] Group 2 - The automotive industry is seen as a potential early adopter for humanoid robots, with several companies exploring applications in manufacturing, although there are concerns about the maturity of the technology [6][8][9] - Companies such as UBTECH and Galaxy General are collaborating with major automotive manufacturers to test humanoid robots in production lines, indicating a growing interest in integrating these technologies into traditional industries [8][9] - Despite the enthusiasm, there are significant challenges related to the complexity of automotive tasks and the high costs associated with humanoid robots, which currently exceed the budgets of many manufacturers [9][10] Group 3 - The shortage of training data for embodied intelligence models is a critical bottleneck in the development of humanoid robots, with companies exploring various strategies to overcome this challenge [11][12] - The reliance on synthetic data for training humanoid robots is highlighted, with companies like Galaxy General focusing on creating large datasets to improve the robots' operational capabilities [12][13] - The practical application of humanoid robots in settings like smart pharmacies is being tested, with the potential for significant cost savings compared to human labor, although challenges remain in executing complex tasks [13][14]