3个月斩获近10亿元融资！清华“姚班”天才、旷视五剑客携手入局具身智能机器人赛道

Core Insights - The article highlights the recent funding success of "Yuanli Lingji" (原力灵机), a robotics startup focused on embodied intelligence, which has raised nearly 1.2 billion yuan in total funding this year [2][5]. Funding Overview - "Yuanli Lingji" completed a series of funding rounds, including an A+ round of approximately 1 billion yuan led solely by Alibaba on November 12, 2025, and an A round led by NIO Capital on August 27, 2025, with a total of nearly 1 billion yuan raised in these two rounds [3][5]. - The company has achieved a total of nearly 1.2 billion yuan in three funding rounds within nine months, starting with a 200 million yuan angel round on March 26, 2025 [3][5]. Company Background - "Yuanli Lingji" was founded on March 6, 2025, in Chongqing by early core technical members of Megvii Technology (旷视科技), including Tang Wenbin, Fan Haoqiang, and Zhou Erjin, focusing on the integration of large model technology with robotics [5][6]. - The founding team has a strong academic background and over ten years of experience in AI product implementation, making them a unique player in the embodied intelligence sector [7]. Technical Capabilities - The company has developed a comprehensive technology stack for embodied intelligence, achieving significant results in benchmark tests and becoming the first in China to exceed a 90% success rate across over a hundred tasks [8]. - "Yuanli Lingji" has created a proprietary end-to-end multi-modal large model (MMLA) that integrates various sensor data and language models, enabling intelligent generalization across different scenarios and tasks [8]. Competitive Landscape - The article notes that "Yuanli Lingji" shares the spotlight with another startup, "Tashi Zhihang," which has also raised significant funding without product launches, indicating a competitive and rapidly evolving market for embodied intelligence startups [10]. - The company is positioned to leverage its unique combination of algorithm, hardware, and application scenarios, which is seen as a competitive advantage in the robotics field [15].