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-making systems, which are attracting significant attention from both capital and 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 [6]. - **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 [6]. - **Wall-OSS**: An open-source embodied intelligence foundational model with strong generalization and reasoning capabilities [6]. - **UBTECH**: Established in 2012, it is a leader in humanoid robot commercialization with comprehensive self-research capabilities [10]. - **Thinker Model**: A multi-modal large model with 10 billion parameters, expected to achieve top rankings in three international benchmark tests by 2025, enhancing robots' perception and task planning in complex environments [10]. - **Zhiyuan Robotics**: Founded in February 2023, it aims to create world-class general embodied intelligent robot products [12]. - **Genie Operator-1**: Set to release in March 2025, it integrates multi-modal large models and hybrid expert technology, improving task success rates by 32% compared to market models [12]. - **Galaxy General**: Founded in May 2023, it focuses on multi-modal large models driven by synthetic data [14]. - **VLA Model**: The world's first general embodied large model, utilizing a "brain + cerebellum" collaborative framework [14]. - **Qianxun Intelligent**: Established in 2024, it specializes in AI and robotics with a strong technical foundation [16]. - **Spirit V1 VLA Model**: The first AI model to tackle long-range operations of flexible objects, supporting multi-task generalization [16]. - **Star Motion Era**: A new tech company incubated by Tsinghua University, focusing on general artificial intelligence applications [18]. - **ERA-42 Model**: The first end-to-end native embodied large model in China, capable of learning over 100 dynamic tasks through video training [18]. International Companies - **Figure AI**: Focuses on developing embodied intelligence large models and related infrastructure for various industries [20]. - **Noematrix Brain**: Combines advanced algorithms and data support for comprehensive capabilities in instruction reasoning and task planning [20]. - **Physical Intelligence**: A startup established in January 2023, aims to create advanced intelligent software for robots [24]. - **π0 Model**: Released on October 31, 2024, it is a foundational model for robots, achieving fine control capabilities through pre-training and fine-tuning [24]. - **Google DeepMind**: Merged with Google Brain in 2023, focusing on general artificial intelligence research [22]. - **Gemini Robotics**: A VLA model that allows robots to perform complex tasks without specialized training, enhancing their adaptability to environmental changes [22]. - **NVIDIA**: A leading GPU design company that has expanded into AI solutions [24]. - **Eureka System**: Based on GPT-4, it can automatically train robots for complex actions and optimize reinforcement learning processes [24].

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 Insights - The article discusses the advancements in embodied intelligence, particularly focusing on the new training paradigm introduced by Zhujidongli with their LimX DreamActor, which utilizes a multi-data approach for training robots [1][3][25]. Group 1: LimX DreamActor Overview - LimX DreamActor integrates video data, simulation data, and real machine data to enhance robot training efficiency and performance [3][17]. - The training process consists of four steps: data collection using consumer-grade devices, 3D reconstruction with physical parameters, extensive training in simulated environments, and fine-tuning on real machines [7][9][15]. Group 2: Data Utilization Strategy - The multi-data strategy addresses the limitations of each data type: real machine data is expensive, simulation data lacks realism, and video data is challenging to apply due to the absence of physical properties [3][17]. - The approach emphasizes data efficiency, aiming to achieve better performance at lower costs by leveraging diverse data sources [3][16]. Group 3: Technical Innovations - DreamActor employs advanced real machine reinforcement learning (RL) techniques, which significantly enhance learning efficiency and the ability to generalize from simulation to real-world applications [16][18]. - The integration of Real2Sim2Real strategies allows for a more reliable deployment of robots, reducing risks and shortening development cycles [18][20]. Group 4: Historical Context and Evolution - LimX DreamActor is an evolution of previous efforts by Zhujidongli, such as LimX VGM, which focused solely on video data for training robots without real machine samples [21][23]. - The transition from VGM to DreamActor reflects a deeper understanding of data application and the pursuit of optimal data-performance ROI [21][23]. Group 5: Industry Implications - The advancements in the multi-data approach are expected to lower the barriers for participation in embodied intelligence development, enabling more teams to engage in this field [25]. - The article suggests that achieving a balance between efficiency and stability in robot training is crucial for the large-scale application of embodied intelligence [25].