Core Viewpoint - The article highlights the successful completion of a 1 billion yuan Series B financing round for Xinghai Map, indicating strong market recognition of its technological advancements and commercial progress in embodied intelligence [2]. Group 1: Financing and Shareholder Structure - The financing round attracted leading industry capital such as Jinding Capital, BAIC Investment, and Bihong Investment, which will accelerate technological integration in smart manufacturing and the automotive industry [4]. - Top-tier private equity funds like Zhengxin Valley Capital and Qianhai Ark's participation reflects long-term value investors' recognition of the company's growth potential [4]. - International capital from Yifeng Capital enhances the company's global strategy and resource access, facilitating overseas market expansion [4]. - The continued investment from five major existing shareholders demonstrates confidence in the company's execution capabilities and growth potential [4]. Group 2: Technological Strength - Xinghai Map maintains a "full-stack self-research" approach, investing in algorithms, hardware, and data to build a comprehensive technology system [5]. - The company has iterated its foundational model, launching the G0 model in August 2025 and its upgraded version G0 Plus in January 2026, which is recognized as the world's first out-of-the-box VLA model [5]. - The open-source dataset from Xinghai Map has been downloaded over 500,000 times, becoming the most widely adopted dataset among major global institutions and enterprises [5]. - The company has established a high-efficiency data collection and model training base, entering a phase of large-scale training with hundreds of thousands of hours of high-quality data [5]. Group 3: Commercialization and Market Position - Xinghai Map has secured thousands of orders, covering top global universities, research institutions, and industry leaders, indicating strong commercial capabilities [7]. - The company leads the market in wheeled dual-arm robots, with its R1 Pro and R1 Lite platforms being utilized by over 90% of top global developers [7]. - The company has received large-scale orders from leading domestic automotive manufacturers and smart logistics companies, marking a transition from feasibility demonstrations to large-scale deployments [8]. Group 4: Vision and Future Goals - Xinghai Map aims to redefine its mission beyond manufacturing single robots, focusing on building foundational infrastructure for the intelligent transformation of the physical world [11]. - The company envisions deploying 10 billion intelligent agents to serve 10 billion people, having established a closed-loop verification from technology research and product innovation to commercial implementation [11].

2月11日，星海图完成10亿元B轮融资，本轮投资方包括一线产业资本金鼎资本、北汽产投、碧鸿投资，顶尖PE基金正心谷资本、前海方舟和国际化基金毅 峰资本，五位主要老股东凯辉基金、美团龙珠、今日资本、襄禾资本和高瓴创投联手超额或满额追加。由此成为中国具身智能企业中老股东持续加注比例和 频次最高的公司。 此轮融资不仅是市场对星海图技术路线与商业化进展的双重认可，更标志着具身智能正从技术突破迈向规模落地的价值拐点。 据「TMT星球」了解，截至本轮，星海图累计融资额近30亿元，估值百亿元，成为继宇树、智元、银河通用之外具身智能行业又一只百亿独角兽，也是这 四家中成立时间最短的企业。 产业与国际化资本深度协同，构筑高质量增长底盘 本轮融资结构呈现鲜明的产业与战略特征，反映出市场对具身智能产业的关注重点，已从早期的技术验证转向对产业协同与商业化落地的深度考量。 •产业资本入局：金鼎资本、北汽产投、碧鸿投资等产业资本的加入，将加速星海图在智能制造、汽车工业等核心场景的技术融合与战略协同。这也与星海 图重点发力的落地场景息息相关。 •顶级PE基金青睐：正心谷资本、前海母基金的青睐，代表着从全周期视角的长期价值投资者对长坡厚雪的 ...

（来源：具身研习社） 出品：具身研习社 2026年2月11日，星海图完成10亿元B轮融资，本轮投资方包括一线产业资本金鼎资本、北汽产投、碧鸿投资，顶尖PE基金正心谷资本、前海方舟和国际 化基金毅峰资本，五位主要老股东凯辉基金、美团龙珠、今日资本、襄禾资本和高瓴创投联手超额或满额追加。由此成为中国具身智能企业中老股东持续 加注比例和频次最高的公司。 此轮融资不仅是市场对星海图技术路线与商业化进展的双重认可，更标志着具身智能正从技术突破迈向规模落地的价值拐点。截至本轮，星海图累计融资 额近30亿元，估值百亿元，成为继宇树、智元、银河通用之外具身智能行业又一只百亿独角兽，也是这四家中成立时间最短的企业。 本轮融资结构呈现鲜明的产业与战略特征，反映出市场对具身智能产业的关注重点，已从早期的技术验证转向对产业协同与商业化落地的深度考量。 •产业资本入局：金鼎资本、北汽产投、碧鸿投资等产业资本的加入，将加速星海图在智能制造、汽车工业等核心场景的技术融合与战略协同。这也与星 海图重点发力的落地场景息息相关。 •顶级PE基金青睐：正心谷资本、前海母基金的青睐，代表着从全周期视角的长期价值投资者对长坡厚雪的赛道和公司的认可。 ...

据称，星海图已实现订单数千台，客户覆盖全球顶尖高校、研究机构及行业领军企业。目前已实现从核 心模组、整机设计、高质量数据、端到端模型及场景解决方案的全栈自研。其R1 Pro和R1 Lite平台也成 为斯坦福大学李飞飞团队、Physical Intelligence（PI）等顶尖实验室的核心开发平台。 新浪财经讯 2026年2月11日，星海图宣布完成10亿元B轮融资，本轮投资方包括产业资本金鼎资本、北 汽产投、碧鸿投资，PE基金正心谷资本、前海方舟和国际化基金毅峰资本，五位主要老股东凯辉基 金、美团龙珠、今日资本、襄禾资本和高瓴创投联手超额或满额追加。 星海图坚持端到端的视觉-语言-动作（VLA）基础模型技术路线，持续推动模型性能与泛化能力的跨 越。公司于2025年8月发布G0 模型，2026年1月推出其升级版 G0 Plus，定位为"全球首个开箱即用的 VLA模型"。同时，公司开源的星海图开放世界数据集下载量已超 50 万次。 星海图构建了全链路、高效率的物理世界数据采集与模型训练基座。2025年，公司在成熟的真机数据采 集体系基础上，创新引入 UMI 等新型采集方式，进一步提升多形态机器人场景数据的采集效率 ...

Core Viewpoint - Xinghaitu has completed a B-round financing of 1 billion yuan, becoming a unicorn in the embodied intelligence sector with a valuation of 10 billion yuan, and has the highest proportion and frequency of continued investment from existing shareholders among its peers [2][4]. Group 1: Financing and Valuation - Xinghaitu has raised nearly 3 billion yuan in total financing, making it one of the few unicorns in the embodied intelligence industry alongside Yushu, Zhiyuan, and Yinhe General, and it is the youngest among them [2]. - The company has received investments from top-tier industry capital and private equity funds, indicating strong market confidence [2]. Group 2: Efficiency and Strategy - Xinghaitu is recognized as the most efficient spender among leading companies in the embodied intelligence sector for 2025, emphasizing a cautious approach to spending in anticipation of future data growth and model training demands [4]. - The company views the industry as a marathon rather than a sprint, focusing on long-term development rather than short-term gains [4]. Group 3: Technological Development - Xinghaitu is committed to an end-to-end visual-language-action (VLA) foundational model technology route, with plans to release a state-of-the-art G0 model in August 2025 and its upgraded version G0 Plus in January 2026 [5]. - The company has achieved over 500,000 downloads of its open-source dataset, making it the most widely adopted dataset in the field [5]. Group 4: Data Collection and Model Training - Xinghaitu has established a high-efficiency data collection and model training base, incorporating innovative data collection methods to enhance the efficiency and accuracy of data gathering [7]. - By 2026, the company aims to enter a phase of large-scale training with hundreds of thousands of hours of high-quality data [7]. Group 5: Commercialization and Market Position - Xinghaitu has secured thousands of orders and has a strong customer base that includes top universities and industry leaders, demonstrating its market competitiveness [11]. - The company leads in the developer market, with its platforms covering over 90% of top global developers, indicating a strong market presence [11]. Group 6: Vision and Future Goals - Xinghaitu aims to redefine its mission beyond manufacturing single robots, focusing on building foundational infrastructure for the intelligent transformation of the physical world [15]. - The company envisions deploying 10 billion intelligent agents to serve 10 billion people, aiming to lead technological iterations and industry integration [16].

Core Viewpoint - The article discusses the evolution and significance of embodied intelligence, emphasizing its philosophical roots and the necessity of physical interaction for intelligent systems [4][5][7]. Group 1: Historical Development - The concept of embodied intelligence traces back to philosophical and cognitive science developments, highlighting the importance of physical interaction in cognitive processes [4]. - Key experiments, such as Richard Held's "passive movement cat" study, demonstrate the intrinsic link between perception and action, reinforcing the idea that active engagement with the environment is crucial for learning [5]. - The shift from traditional views of intelligence as disembodied computation to a more integrated approach that includes physical embodiment is outlined [6][7]. Group 2: Current Trends in Embodied Intelligence - The construction of immersive environments for embodied intelligence is essential, requiring the integration of physical properties and sensory feedback [9][10]. - The development of large-scale, systematic robot training facilities is identified as a critical infrastructure for advancing embodied intelligence [12]. - Various high-level robot training platforms are emerging across China, indicating a rapid growth in this sector [12]. Group 3: Data Collection and Training - High-quality, diverse behavioral data is crucial for the development of embodied intelligence, focusing on visual, interaction, and semantic understanding data [15][17]. - The article outlines the importance of structured data collection methods, including teleoperation and wearable devices, to enhance the training of robots [19][20]. - A systematic approach to data collection is emphasized, with a focus on stability in object grasping tasks, leading to improved predictive capabilities in robotic systems [22][23][25]. Group 4: Future Directions and Challenges - The integration of embodied intelligence with large models is seen as a key pathway for advancing robotic technology, emphasizing the need for a collaborative framework between edge and cloud computing [26][29]. - The article discusses the necessity of building a comprehensive training ecosystem that combines real and virtual environments to facilitate effective learning and adaptation [34][35]. - The future of embodied intelligence relies on diverse embodied agents and a robust learning and evolution framework to ensure continuous improvement and adaptability [31][36]. Group 5: Practical Applications - Embodied intelligence is being applied in various sectors, including logistics, consumer electronics, and healthcare, showcasing its potential to address real-world challenges [30][33]. - The establishment of training centers and collaborative platforms is crucial for fostering innovation and standardization in the field of embodied intelligence [42][45]. - The article highlights the importance of open-source ecosystems and collaborative efforts among industry players to drive advancements in embodied intelligence [74].