以下文章来源于融中财经 ，作者吕敬之 融中财经 . 中国领先的股权投资与产业投资媒体平台。聚焦报道中国新经济发展和创新投资全产业链。通过全媒体 资讯平台、品牌活动、研究服务、专家咨询、投资顾问等业务，为政府、企业、投资机构提供一站式专 业服务。 导语：资本退潮，百家机器人企业面临生死局。 2025年，人形机器人行业经历了冰与火的双重考验。 一边是 570 亿元融资涌入、近 30 家企业扎堆冲刺上市、亿元级订单频频落地；一边是硅谷明 星创业公司在量产前夜轰然倒塌，协作机器人先驱二次死亡，就连扫地机器人鼻祖也黯然申请破 产保护。 中国 100 多家人形机器人企业经过近三年奔跑，头部与尾部的差距已是云泥之别 ——第一梯队 手握十亿级概念验证订单筹备上市，而那些融资停滞、产品难以落地的企业，用一位行业观察者 的话说， " 事实上已经不行了，只是还保持着低速运转 " 。 技术专家和行业分析师都在发出同样的警告： 2026 年，行业出清将加速到来。 这不是一个关 于失败者的挽歌，而是一面照出行业真实面貌的镜子：当潮水退去，谁在裸泳？当资本从 " 看 Demo" 转向 " 看量产 " ，谁能真正活下来？ 行业分化加剧，淘汰 ...

2025年，人形机器人行业经历了冰与火的双重考验。 一边是570亿元融资涌入、近30家企业扎堆冲刺上市、亿元级订单频频落地；一边是硅谷明星创业公司 在量产前夜轰然倒塌，协作机器人先驱二次死亡，就连扫地机器人鼻祖也黯然申请破产保护。 中国100多家人形机器人企业经过近三年奔跑，头部与尾部的差距已是云泥之别——第一梯队手握十亿 级概念验证订单筹备上市，而那些融资停滞、产品难以落地的企业，用一位行业观察者的话说，"事实 上已经不行了，只是还保持着低速运转"。技术专家和行业分析师都在发出同样的警告：2026年，行业 出清将加速到来。这不是一个关于失败者的挽歌，而是一面照出行业真实面貌的镜子：当潮水退去，谁 在裸泳？当资本从"看Demo"转向"看量产"，谁能真正活下来？ 行业分化加剧，淘汰赛已经开始 2025年，人形机器人行业经历了一场残酷的淘汰赛。曾经站在聚光灯下的明星企业接连倒下，硅谷的创 业神话被现实击碎，就连开创协作机器人品类的行业先驱也未能幸免。当潮水退去，技术的狂热与商业 的冷酷之间那道难以逾越的鸿沟终于显现。 最具戏剧性的倒闭发生在2025年11月。硅谷人形机器人初创公司K-Scale Labs的创始人本 ...

Core Viewpoint - Despite significant investments from venture capital firms and large tech companies, humanoid robots still struggle to achieve dexterity, which is essential for performing tasks in human environments [2][3][4]. Group 1: Historical Context of Humanoid Robots - The concept of humanoid robots has been explored for over 65 years, with early developments including a computer-controlled robotic arm capable of stacking blocks in 1961 [3]. - The evolution of humanoid robots has seen contributions from various institutions, including WABOT-1 from Waseda University in the 1970s and Honda's ASIMO in 2000 [11][12]. Group 2: Current State and Future Predictions - Humanoid robots are currently in the early stages of development, with Gartner indicating they have not yet reached their peak hype [4]. - Companies like Tesla and Figure are optimistic about the economic potential of humanoid robots, with predictions of creating trillions in revenue [9][10]. Group 3: Challenges in Dexterity - Achieving human-level dexterity in humanoid robots remains a significant challenge, as current robotic hands lack the necessary finesse and adaptability for a wide range of tasks [23][24]. - Existing methods for training robots often rely on visual demonstrations, which do not adequately capture the tactile feedback necessary for dexterous manipulation [27][28]. Group 4: Learning Approaches - The industry has seen a shift towards end-to-end learning methods, where robots learn from observing human actions, but this approach has limitations due to the lack of tactile feedback and precision [30][31]. - Successful applications of end-to-end learning in other fields, such as speech recognition and image labeling, highlight the importance of pre-processing and human-like structures in achieving effective learning outcomes [49][50]. Group 5: Importance of Tactile Feedback - Human dexterity is heavily reliant on rich tactile feedback, which current humanoid robots do not possess, leading to challenges in replicating human-like manipulation [51][52]. - The complexity of human touch perception and the integration of multiple body parts in dexterous tasks further complicate the development of humanoid robots capable of similar actions [52].