2025.08. 11 本文字数：1430，阅读时长大约3分钟 作者 | 第一财 经 刘佳 在世界机器人大会上,宇树CEO王兴兴一口气提了不少"非共识"。他对 VLA (Vision-Language-Action视觉-语言-动作)模型持怀疑态度, 认为 这属于"相对傻瓜式架构"；他还说机器人行业对数据关注度有点太高了，包括灵巧手在内的硬件虽然不够好但够用，行业最大的问题在于具 身智能的AI完全不够用。 王兴兴的观点在业内持续引发讨论。今日世界机器人大会上，记者留意到，国家地方共建人形机器人创新中心首席科学家江磊近20分钟的演 讲中，3次提到了王兴兴。 对于王兴兴关于"硬件足够用、大模型不够用"的观点，江磊分享了与阿里、华为等企业交流的体会："我们是选不到一个很好的身体"，并坦 承今天行业确实还用不上全参数模型，机器人的大脑、小脑、肢体需要深度协同；王兴兴质疑VLA并尝试用视频生成驱动机器人任务，江磊 承认"感知-认知-决策-执行的闭环尚未闭合"，呼吁重构VLA模型，寻求新的解决范式；王兴兴还提到，机器人在RL（强化学习）的Scaling law（尺度定律）是非常值得做的方向，江磊认同表示，强化学习跟模仿学习 ...

传统的人形机器人面临感知局限、决策断层、泛化瓶颈三大核心挑战。 在世界机器人大会上,宇树CEO王兴兴一口气提了不少"非共识"。他对 VLA (Vision-Language-Action视 觉-语言-动作)模型持怀疑态度, 认为这属于"相对傻瓜式架构"；他还说机器人行业对数据关注度有点太 高了，包括灵巧手在内的硬件虽然不够好但够用，行业最大的问题在于具身智能的AI完全不够用。 王兴兴的观点在业内持续引发讨论。今日世界机器人大会上，记者留意到，国家地方共建人形机器人创 新中心首席科学家江磊近20分钟的演讲中，3次提到了王兴兴。 对于王兴兴关于"硬件足够用、大模型不够用"的观点，江磊分享了与阿里、华为等企业交流的体 会："我们是选不到一个很好的身体"，并坦承今天行业确实还用不上全参数模型，机器人的大脑、小 脑、肢体需要深度协同；王兴兴质疑VLA并尝试用视频生成驱动机器人任务，江磊承认"感知-认知-决 策-执行的闭环尚未闭合"，呼吁重构VLA模型，寻求新的解决范式；王兴兴还提到，机器人在RL（强 化学习）的Scaling law（尺度定律）是非常值得做的方向，江磊认同表示，强化学习跟模仿学习都需要 进入Scalin ...

Core Viewpoint - The article discusses the emergence of embodied intelligence in China, highlighting the rapid growth and investment in the sector, particularly focusing on the company "Self-Variable Robotics" founded by Wang Qian, which has raised over 1 billion yuan in funding within a year and a half [5][12]. Group 1: Company Overview - Wang Qian, the founder of Self-Variable Robotics, has a strong academic background and prior experience in the U.S. quant fund industry, which he left to pursue robotics [2][5]. - Since its establishment in 2023, Self-Variable Robotics has completed seven rounds of financing, with a total amount exceeding 1 billion yuan [5]. - The company has adopted an "end-to-end unified VLA model" technology route, updating its model every 2-3 months [7][12]. Group 2: Industry Context - 2023 is marked as a significant year for the domestic embodied intelligence sector, with major players like Nvidia's founder predicting it as the next tech wave [5]. - The domestic humanoid robotics startup landscape has formed a clear hierarchy, with Self-Variable Robotics moving from a secondary to a quasi-first-tier position due to its funding achievements [5]. - There are contrasting views on the commercial viability of humanoid robots, with some investors skeptical about their practical applications, while others continue to invest heavily [5][10]. Group 3: Technological Development - Self-Variable Robotics has developed the WALL-A model capable of performing complex tasks beyond simple operations, positioning itself at the forefront of the industry [8][12]. - Wang Qian anticipates that a GPT-3 level embodied intelligence model could emerge within a year, with commercial applications expected to materialize in one to two years [10][21]. - The company prioritizes enhancing model capabilities over immediate commercialization, with two-thirds of its expenditures directed towards model development [12][30]. Group 4: Market and Commercialization - Current commercial applications for embodied robots are primarily in research education and hospitality, which Wang Qian believes are not the ultimate target markets for long-term growth [10][31]. - The company has already developed a physical product, although it has not yet been widely released, and is currently in the proof of concept stage with seed customers [27][29]. - Wang Qian expresses skepticism about the long-term value of current commercial scenarios, suggesting they may be more about meeting investor expectations than achieving substantial market impact [31][32]. Group 5: Competitive Landscape - The article notes that while domestic companies are catching up, there remains a significant gap between Chinese and U.S. companies in terms of overall capabilities [37]. - Self-Variable Robotics claims to be on par with international leaders like Physical Intelligence and Google in certain aspects, despite the general perception of being behind [38]. - The challenges of open-source models in the embodied intelligence space are highlighted, with Wang Qian arguing that commercial success cannot rely solely on open-source strategies [43][44].