点击下方 卡片 ，关注" 具身智能 之心 "公众号 当前，具身智能已成为全球的新焦点，如何打造一个通用的本体和大脑是各个创业公司一直努力突破 的，更是受到资本和产业界的高度关注。 我们今天为大家全面梳理具身大脑领域的国内外知名公司，深入分析其技术特点、产品布局和应用场 景，为公司提供行业全景图，助力战略决策和业务拓展。 重点关注 ：聚焦于开发机器人 "大脑" 系统的企业，包括具身大模型、多模态感知决策系统等。 （一）国内公司 自变量机器人（CEO 王潜） 星海图（CEO 高继扬） 公司简介 ：成立于 2023 年，聚焦 "通用具身大模型" 研发，以 真实世界数据 为主要数据来源构建 具备精细操作能力的通用机器人。在技术路线上更偏向于 "大脑"，从一开始就坚持走 端到端的具身 通用大模型路线 。成立不到两年，已完成 8 轮融资。 代表成果 ： WALL - A 模型：2024 年 10 月推出全球目前最大参数规模的具身智能通用操作大模型Great Wall 系列（GW）的 WALL - A 模型，能整合视觉、语言与运动控制信号，实现从感知到执行的完整 闭环，跨任务泛化能力出色。 开源具身智能基础模型Wall-O ...

Core Viewpoint - The article provides a comprehensive overview of notable companies in the field of embodied intelligence, focusing on their technological characteristics, product layouts, and application scenarios, which are crucial for strategic decision-making and business expansion in the industry [2][3]. Domestic Companies - **Xinghai Map**: Founded in 2023, focuses on developing a "general embodied large model" using real-world data to create robots with fine operational capabilities. The company has completed 8 rounds of financing [5]. - **WALL-A Model**: Set to launch in October 2024, it will be the largest parameter scale embodied intelligence general operation model globally, integrating visual, language, and motion control signals [5]. - **Wall-OSS**: An open-source foundational model with strong generalization and reasoning capabilities [5]. - **UBTECH**: Established in 2012, a leader in humanoid robot commercialization with comprehensive self-research capabilities [6]. - **Thinker Model**: A hundred billion parameter multimodal model set to be developed by 2025, achieving top results in three international benchmark tests [6]. - **Zhiyuan Robotics**: Founded in February 2023, focuses on deep integration of AI and robotics [7]. - **Genie Operator-1**: A multimodal large model set to release in March 2025, enhancing task success rates by 32% compared to market models [7]. - **Galaxy General**: Established in May 2023, known for its core technology and products that create three major technical barriers [8]. - **VLA Model**: The world's first "general embodied large model" developed independently, utilizing a "brain + cerebellum" collaborative framework [8]. - **Qianxun Intelligent**: Founded in 2024, focuses on AI + robotics with a strong technical background [10]. - **Spirit V1 VLA Model**: The first model to tackle flexible object long-range operation challenges, supporting complex task execution through visual-language-action integration [10]. - **Star Motion Era**: A new tech company incubated by Tsinghua University, focusing on general artificial intelligence applications [11]. - **ERA-42 Model**: The first end-to-end native embodied large model in China, capable of learning over 100 dynamic tasks [11]. Foreign Companies - **Figure AI**: Focuses on embodied intelligence operation algorithms, enhancing data training and algorithm performance [16]. - **LimX DreamActor**: A new training paradigm combining simulation and real-world data for embodied intelligence training [16]. - **Physical Intelligence**: Founded in January 2023, aims to develop advanced intelligent software for various robots [21]. - **π0 Model**: Released in October 2024, a universal robot foundational model with pre-training and fine-tuning capabilities [21]. - **Google DeepMind**: Merged with Google Brain in 2023, focusing on general artificial intelligence research [19]. - **Gemini Robotics**: A VLA model that can control robots for complex tasks without specialized training [19]. - **Skild AI**: A leading robotics "brain" development company in the US, aiming to create a universal robot operating system [25]. - **Eureka System**: Based on GPT-4, it can automatically train robots for complex actions and optimize reinforcement learning processes [25].

Core Insights - The article focuses on the emerging field of embodied intelligence, highlighting the development of general-purpose robotic "brain" systems and multi-modal perception-decision systems, which are gaining significant attention from both capital and industry sectors [2][3]. Domestic Companies - **Xinghai Map**: Founded in 2023, focuses on developing a general embodied large model using real-world data to create robots with fine operational capabilities. The company has completed 8 rounds of financing in less than two years. Its representative product, WALL-A model, is set to launch in October 2024 and is claimed to be the largest parameter scale embodied intelligence model globally, integrating visual, language, and motion control signals [6]. - **UBTECH**: Established in 2012, it is a leader in humanoid robot commercialization with comprehensive self-research capabilities. The Thinker model, set to be released in 2025, has achieved top rankings in international benchmark tests, significantly enhancing robots' perception and planning capabilities in complex environments [10]. - **ZhiYuan Robotics**: Founded in February 2023, it aims to create world-class general embodied intelligent robots. Its Genie Operator-1 model, to be released in March 2025, integrates multi-modal large model and mixed expert technologies, improving task success rates by 32% compared to market models [12]. - **Galaxy General**: Established in May 2023, it focuses on multi-modal large models driven by synthetic data. Its VLA model is the first general embodied large model globally, utilizing a "brain + cerebellum" collaborative framework [14]. - **Qianxun Intelligent**: Founded in 2024, it is a leading AI + robotics company with a focus on flexible object manipulation. Its Spirit V1 VLA model is the first to tackle long-range operations of flexible objects [16]. - **Star Motion Era**: A new tech company incubated by Tsinghua University, focusing on general artificial intelligence applications. Its ERA-42 model supports over 100 dynamic tasks through video training [18]. - **Zhujidi Power**: Concentrates on embodied intelligent robots, developing core technologies for hardware design, full-body motion control, and training paradigms [20]. International Companies - **Figure AI**: Focuses on embodied intelligence operation algorithms, enhancing data training and algorithm performance through video generation technology [17]. - **Physical Intelligence**: Founded in January 2023, it aims to develop advanced intelligent software for various robots. Its π0 model, released in October 2024, is a universal robot foundation model [22]. - **Google DeepMind**: Merged with Google Brain in 2023, it focuses on general artificial intelligence research. Its Gemini Robotics model can control robots to perform complex tasks without specialized training [20]. - **Skild AI**: A leading robotics "brain" development company in the US, aiming to create a universal robot operating system that enables intelligent operations across various scenarios [26].

Core Viewpoint - The article discusses the skepticism of Wang Xingxing, CEO of Yushu, regarding the VLA (Vision-Language-Action) model, suggesting that the robotics industry is overly focused on data while lacking sufficient embodied intelligence in AI [3][4]. Group 1: Challenges in Robotics - The traditional robotics industry faces three core challenges: perception limitations, decision-making gaps, and generalization bottlenecks [6][7]. - Current robots often rely on preset rules for task execution, making it difficult to understand complex and dynamic environments [6]. - In multi-task switching, traditional robots frequently require human intervention for reprogramming or strategy adjustments [6]. - Robots need extensive retraining and debugging when confronted with new tasks or scenarios [6]. Group 2: Need for Model Reconstruction - There is a call within the industry to reconstruct the VLA model and seek new paradigms for embodied intelligence [5][7]. - Jiang Lei emphasizes the need for a complete system that integrates both hardware and software, rather than merely relying on large language models [6]. - The current research landscape is fragmented, with large language model researchers focusing solely on language, while edge intelligence concentrates on smaller models [6]. Group 3: Future Directions - Jiang Lei proposes exploring cloud and edge computing collaboration to create a comprehensive deployment architecture for humanoid robots [6]. - The ideal "brain" model for humanoid robots should possess full parameter capabilities, while the "small brain" model deployed on the robot must achieve breakthroughs in size and real-time performance [6]. - The industry is optimistic about humanoid robots becoming a significant sector, with this year being referred to as the year of mass production for humanoid robots [7].

Core Viewpoint - The traditional humanoid robots face three core challenges: perception limitations, decision-making gaps, and generalization bottlenecks [5] Group 1: Industry Challenges - The industry is currently unable to utilize full parameter models effectively, indicating a need for deeper collaboration between the robot's brain, cerebellum, and limbs [2] - Traditional robots often rely on preset rules for task execution, making it difficult to adapt to complex and dynamic environments [5] - Robots require manual intervention for reprogramming or strategy adjustments during multi-task switching [5] Group 2: Perspectives on VLA Model - The VLA (Vision-Language-Action) model is seen as a controversial yet pivotal paradigm for humanoid robot motion control, with many in the industry betting on its potential [4] - The OPEN VLA, based on the Llama2 language model with 7 billion parameters, is an example of a smaller-scale model that still faces challenges in effectively utilizing large language models [4] - There is a call for the industry to explore the collaborative distribution of computing power between cloud and edge devices to create a comprehensive deployment architecture [4] Group 3: Future Directions - The ideal "brain" model for humanoid robots should not only be a large language model but a complete system that deeply integrates hardware and software [4] - The industry is encouraged to rethink the VLA model and seek new paradigms, potentially through biomimicry to develop original foundational models for embodied intelligence [6] - There is growing confidence in the humanoid robot industry, with many believing it will become a significant sector, marking this year as a potential turning point for mass production [6]

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].