Core Viewpoint - Embodied Artificial Intelligence (EAI) is a key driver for future productivity transformation, with China's strategic focus on advancing the industry from technology validation to large-scale commercialization, aiming to lead rather than follow in the global market [1][3]. Group 1: Development of EAI in China - The Chinese government has identified EAI as a strategic priority in the 2025 Government Work Report, emphasizing its role in nurturing emerging industries [1]. - The National and Local Joint Innovation Center for Humanoid Robots was established in May 2024, serving as the first national public platform in the humanoid robot field to promote data collection, technology research, enterprise incubation, and talent cultivation [1][3]. Group 2: Training Ground Functions - The training ground aims to achieve five core functions: 1. Data collection and scaling, allowing humanoid robots to gather extensive data through environmental interactions to optimize algorithms and enhance performance [4]. 2. Model training and development, providing infrastructure for humanoid robots to improve skills through methods like imitation learning and reinforcement learning [5]. 3. Scene simulation and application implementation, enabling robots to adapt to various tasks in real-world scenarios such as elderly care and industrial inspections [7]. 4. Model testing and evaluation, focusing on assessing key performance indicators like mobility and decision-making logic [8]. 5. Talent cultivation and ecosystem development, offering practical opportunities for researchers and engineers while promoting industry innovation [9]. Group 3: Huawei's Role in EAI Development - Huawei is assisting the National and Local Joint Innovation Center in building a comprehensive solution for the training ground, covering the entire lifecycle from data collection to model training [11]. - Key initiatives include: 1. Efficient and stable edge data collection solutions using a "cloud-edge" architecture to ensure high-quality data acquisition [13]. 2. A robust data management system that supports real-time data processing and enhances data availability while reducing storage costs by 50% [15][16]. 3. Enabling model training through high-performance caching solutions to maximize computational resource utilization [17]. 4. Fostering ecosystem collaboration by creating a shared dataset, "White Tiger Dataset v0.0.1," which is the first globally to exceed one million heterogeneous robot data points [19][20]. Group 4: Industry Impact - The humanoid robot training ground is expected to fill the gap in large-scale heterogeneous humanoid robot datasets, accelerating the development of EAI technology in China and promoting the industrialization and large-scale application of humanoid robots [20].

Core Viewpoint - The article highlights the growth and development of the embodied intelligence community, emphasizing the establishment of a platform for knowledge sharing and collaboration among professionals in the field [1][11]. Group 1: Community Development - The community aims to reach a scale of 2000 members, reflecting significant growth in interest and participation in embodied intelligence [1]. - Various technical routes have been organized internally, providing resources for newcomers and experienced individuals to enhance their knowledge and skills [1][7]. - The community has invited numerous industry experts to engage with members, facilitating discussions on current trends and challenges in embodied intelligence [1]. Group 2: Job Opportunities - The community has established a job referral mechanism with multiple companies in the embodied intelligence sector, allowing members to submit their resumes for potential job openings [2][16]. - Members are encouraged to connect with nearly 200 companies and institutions to discuss the latest industry and academic developments [5][16]. Group 3: Educational Resources - A comprehensive collection of over 30 technical routes and 40+ open-source projects has been compiled to assist members in their learning journey [11][26]. - The community provides access to various datasets, simulation platforms, and learning materials tailored for different aspects of embodied intelligence [30][32]. - Regular discussions and forums are held to address common questions and share insights on topics such as robot simulation, imitation learning, and decision-making processes [12][66]. Group 4: Industry Insights - The community aggregates research reports and industry analysis related to embodied intelligence, enabling members to stay informed about advancements and applications in the field [19][24]. - A directory of domestic and international companies involved in embodied intelligence is available, covering various sectors such as education, logistics, and healthcare [17].

Core Insights - The article highlights the growth and development of the embodied intelligence community, aiming to expand to a scale of 2000 members, showcasing various projects and initiatives in the field [1][5]. Group 1: Community Development - The community has witnessed significant advancements with the introduction of various projects such as ACT, RDT-1/RDT-2, CogACT, OpenVLA, π0, and π0.5 [1]. - A total of over 30 technical routes have been organized internally to assist members in finding benchmarks, reviews, and learning pathways, significantly reducing search time [1]. - The community has invited numerous industry experts to engage with members, providing opportunities for Q&A sessions and discussions on the latest developments in embodied intelligence [1]. Group 2: Job Opportunities and Networking - The community has established a job referral mechanism with multiple embodied intelligence companies, facilitating members in submitting their resumes to desired companies [2]. - Members are encouraged to join the community to connect with nearly 200 companies and institutions in the embodied intelligence sector, fostering collaboration and knowledge sharing [5]. Group 3: Educational Resources - The community has compiled a wealth of resources for newcomers, including over 40 open-source projects and nearly 60 datasets related to embodied intelligence [11]. - Various learning paths have been outlined, covering topics such as reinforcement learning, multi-modal large models, and robot navigation, catering to both beginners and advanced members [11][12]. - Regular discussions and sharing sessions are held to address common questions in the field, such as robot simulation platforms and imitation learning for humanoid robots [12]. Group 4: Industry Insights - The community provides a comprehensive overview of domestic and international embodied intelligence companies, covering various sectors such as education, logistics, and healthcare [17]. - Members have access to a collection of industry reports and academic papers, enabling them to stay updated on the latest trends and applications in embodied intelligence [19]. - The community also offers insights into the latest advancements in robotics, including navigation, planning, and multi-modal model integration [41][49].

Investment Rating - The industry investment rating is "Overweight" indicating an expected outperformance of the industry index relative to the benchmark by more than 5% in the next six months [81]. Core Insights - Data is the key driver for the rapid breakthroughs and practical applications of embodied intelligence technology, similar to the path of autonomous vehicles. High-quality datasets are essential for training and deploying intelligent agents to effectively complete complex tasks [3][17]. - There is a current scarcity of high-quality and diverse datasets for embodied intelligence, which is crucial for the training of robots. The need for standardized and validated datasets is a pressing requirement in the industry [3][17]. - The report emphasizes the importance of both real and simulated data for training embodied intelligence models, highlighting their complementary roles in the data collection process [22][24]. Summary by Sections 1. Basic Concepts of Embodied Intelligence Datasets - Embodied intelligence datasets are categorized into real data and simulated data, with real data collected through physical interactions and simulated data generated in virtual environments [22][24]. 2. Current Status of Domestic and International Real Datasets - Various high-quality embodied intelligence datasets have been released, such as AgiBot World and Open X-Embodiment, showcasing a wide range of tasks and skills [30][31]. 3. Current Status of Domestic and International Simulated Datasets - The report discusses the technological pathways for scene generation and simulation in creating simulated datasets, emphasizing the importance of both methods in training [50][51]. 4. Related Companies - Key companies to watch in the embodied intelligence data sector include Junsheng Electronics, Haitian Ruisheng, Suochen Technology, and Huaru Technology, which are involved in data collection and simulation [76].