Core Viewpoint - The FCloud OmniBot Empowerment Salon focused on the development of embodied intelligence technologies, emphasizing the importance of physical simulation and data synthesis for scaling applications in the robotics industry [1][3][20]. Group 1: Industry Development Opportunities - The event gathered experts from academia, research institutions, and industry to discuss new opportunities for industrial development [3][5]. - Zhangjiang Science City has over 1,000 AI companies, with more than 90 in the field of embodied intelligence, forming a complete industrial chain from core components to complete machine development [5][20]. Group 2: FCloud OmniBot Platform - FCloud has established a 2,000-card computing center in Zhangjiang to support local enterprises, with plans for further expansion [7][9]. - The OmniBot platform addresses three main challenges in embodied intelligence development: simulation environment setup, synthetic data generation, and computing power requirements [9][20]. - OmniBot integrates NVIDIA Isaac Sim and Isaac Lab for high-performance simulation capabilities, allowing developers to access simulation software via cloud desktops without complex local setups [11][20]. Group 3: Technical Innovations - The platform can generate 100 synthetic data points from a single real-world data point, significantly enhancing data collection efficiency [11][20]. - OmniBot supports cloud training and deployment of mainstream models, including specialized models for embodied intelligence [12][20]. - The cloud-edge collaboration model allows developers to train models in the cloud and deploy them on robots, reducing development costs and barriers [12][20]. Group 4: Academic and Technical Sharing - The salon featured discussions on the data gap in the robotics field, highlighting that training data for robots is 6,500 times less than that for large language models [13][20]. - Research from Shanghai Jiao Tong University introduced a novel instruction expression method that improves efficiency and generalization capabilities [15][20]. Group 5: Open Ecosystem and Collaboration - FCloud OmniBot emphasizes ecosystem development, welcoming partnerships from various stakeholders, including robot manufacturers and algorithm developers [18][20]. - The platform operates on a SaaS model, providing flexible access and special policies for students and individual developers to encourage participation [18][20]. Group 6: Future Trends and Prospects - The trend towards simulation-first development is becoming mainstream, with physical simulation seen as key to addressing data scarcity and reducing development costs [20]. - The integration of cloud-edge collaboration is essential for meeting the increasing complexity of robotic tasks [20]. - The continuous decline in computing costs and improvements in simulation technology are expected to lead to large-scale applications of embodied intelligence within the next 3-5 years [20][21].

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].

Core Viewpoint - The 2025 Science & Technology Innovator Conference focuses on "Embodied Intelligence as a New Engine for Industrial Transformation," emphasizing the commercialization of hard technology and addressing the challenges of transitioning from technology to product [3][5]. Event Overview - The conference is organized by the Zhiyou Yari Innovation Hub and will take place on September 5, 2025, at the Wanli Hotel in Beijing's Zhongguancun Dongsheng Science Park [6]. - The event will feature various segments including award ceremonies, report releases, keynote speeches, peak dialogues, roundtable forums, and thematic presentations, covering the entire value chain of embodied intelligence [5][6]. Key Themes and Services - The conference aims to create a "three libraries and four chains" service system, which includes an industry library for precise demand matching, a project library for selecting quality technologies, and a talent library for gathering innovative forces [3]. - The focus will be on providing a full-chain service model that includes demand-driven technology matching, capital support, and real-world scenario validation for cutting-edge technologies like embodied intelligence [3]. Notable Speakers and Topics - Keynote speakers include Paolo Dario, Huang Tiejun, and Wang Tiancao, who will discuss topics ranging from robotics' role in health to the evolution of embodied intelligence technology [7][8]. - The conference will also feature discussions on the commercialization challenges and breakthroughs in embodied intelligence, with insights from industry leaders and researchers [8][9]. Industry Impact - The conference is expected to attract top figures in the embodied intelligence field, facilitating knowledge exchange and strategic management insights from macro trends to micro case studies [5][8]. - It aims to address the "last mile" challenge in technology-to-product transition, providing a platform for real-world application and scaling of advanced technologies [3][5].

Core Viewpoint - Continuous robots exhibit great potential in fields such as robotic surgery and narrow space detection, but precise control remains a significant challenge. Recent breakthroughs by research teams from City University of Hong Kong and Hefei University of Technology have applied Kalman filtering technology to enhance the online control precision of these robots [1][2]. Group 1: Continuous Robots and Control Challenges - Continuous robots possess infinite degrees of freedom and adaptability, making them difficult to control accurately due to their deformable nature, akin to controlling a cooked noodle [4]. - Traditional rigid-link robots have simpler control mechanisms, while continuous robots face challenges from large deformations, friction effects, and inherent non-linear characteristics [4][5]. - The research team designed a lightweight robot with a complex internal structure, consisting of three flexible segments, each with five spacer disks and one drive disk, weighing only 8.4 grams [5]. Group 2: Control Methodology - The team utilized a piecewise constant curvature (PCC) model for initial control, which, while computationally efficient, resulted in position errors exceeding 1.6 mm and angle errors over 1.4 degrees, unacceptable for high-precision applications [7]. - Instead of developing a more complex model, the team innovatively employed the Kalman filter to allow the robot to self-correct during motion, estimating and compensating for errors in real-time [8][9]. - The control system operates at a frequency of 20 Hz, integrating steps such as obtaining end pose, calculating model Jacobians, estimating and compensating for Jacobian errors, and generating control commands [11]. Group 3: Experimental Validation - The research team conducted three trajectory tracking experiments and two disturbance resistance tests, demonstrating the effectiveness of the new method [12]. - In the first experiment, the root mean square error (RMSE) in the x-direction improved from 1.6 mm to 1.1 mm, and in the y-direction from 2.3 mm to 2.1 mm, showcasing significant enhancements in tracking precision [12][14]. - The second experiment focused on attitude control, achieving a reduction in RMSE from 2.1 degrees to 1.5 degrees, while maintaining position accuracy [14]. - The robustness of the method was further validated through disturbance tests, where the robot maintained performance even under significant load changes [15]. Group 4: Innovation and Future Prospects - The research combines model-driven and data-driven approaches, leveraging the strengths of both to enhance control precision while maintaining computational efficiency [17]. - The method's advantages include no need for offline data collection, high computational efficiency, and robustness against external disturbances, indicating strong potential for practical applications [17]. - Future research directions include incorporating dynamic effects and expanding to three-dimensional motion to improve estimation accuracy and applicability [17].

Group 1: Humanoid Robots - Ubtech has secured a procurement contract worth 250 million yuan for humanoid robot products and solutions, primarily featuring the Walker S2 model, with delivery planned for this year [1][3] - The total contract value for Ubtech's Walker series humanoid robots has reached nearly 400 million yuan, including approximately 50 million yuan in delivered orders [3] - Ubtech has established collaborations with companies like BYD, Foxconn, and SF Express in sectors such as new energy vehicle manufacturing, 3C manufacturing, and smart logistics [3] Group 2: Smart Elderly Care Robots - A joint expert group from the Ministry of Industry and Information Technology and the Ministry of Civil Affairs has selected 32 pilot projects for smart elderly care robots, indicating a shift towards large-scale growth in this industry [4][6] - The selected projects include intelligent emotional companionship robots, AI-assisted exoskeletons, and smart nursing collaborative elderly care robots [6] Group 3: AI Startups - French AI startup Mistral AI is completing a funding round of 2 billion euros, with an expected valuation of 14 billion dollars, making it one of the most valuable tech startups in Europe [7][9] - Mistral AI focuses on open-source language models and AI chatbots tailored for the European market, with its valuation previously at 5.8 billion euros [9] - Investment in European AI startups has seen a significant increase, with a 55% year-on-year growth in the first quarter of 2025 [9] Group 4: Robotics Research - A new multi-robot motion planning method based on Graph Neural Networks (GNN) and reinforcement learning has been published in Science Robotics, developed by Google DeepMind Robotics, Intrinsic, and University College London [13][15] - This method allows for the automatic generation of collision-free trajectories by mapping robots, tasks, and obstacles as graph nodes and edges, reducing the need for manual programming [15] Group 5: Investment in Robotics - Shoucheng Holdings has made a multi-million yuan additional investment in Songyan Power to accelerate humanoid robot research and development [10][12] - The investment aims to enhance R&D efforts and product iterations, solidifying Songyan Power's leading position in humanoid robots and bionic facial technology [12]

Group 1 - The core viewpoint of the article highlights the increasing significance of robotics in various sectors, particularly in the context of recent events and developments in the industry [1][5][9][14][18]. Group 2 - The Shanghai Cooperation Organization summit showcased advanced humanoid robots capable of multilingual interaction and various services, indicating a strong focus on technology integration in international events [1]. - Yushu Technology plans to submit an IPO application between October and December 2023, with its quadruped and humanoid robots projected to contribute 65% and 30% to its revenue respectively in 2024, signaling growth potential in the robotics sector [5]. - Apple is pushing for automation in its supply chain by requiring suppliers to invest in robotic technology, which may reduce labor dependency and stabilize product quality, although it poses short-term financial challenges for suppliers [9][12]. - Quicktron, supported by Alibaba, has reportedly applied for an IPO in Hong Kong, aiming to raise at least $100 million, highlighting the competitive landscape in the intelligent warehousing robotics market [14]. - The Recirculate project, funded by the EU, has developed an AI-driven system for dismantling electric vehicle batteries, showcasing innovation in recycling and sustainability within the robotics field [18].

Core Insights - The article discusses the launch of a series of policies aimed at promoting the development of the "Artificial Intelligence + Robotics" sector in Huangpu District, Guangzhou, with a focus on high-quality industrial growth and innovation [2][6][24]. Policy Initiatives - Huangpu District has introduced four major policy areas with nearly 40 specific measures to support the development of artificial intelligence, embodied intelligence, integrated circuits, and new display technologies, with financial support reaching up to 10 million yuan for qualifying projects [2][10][14]. - The policies aim to activate industrial momentum through innovation and provide substantial financial incentives for companies engaged in AI and robotics [24][20]. Industry Development Focus - The article highlights Huangpu's strategic positioning in the AI sector, emphasizing its complete industrial chain, abundant computing resources, and rich application scenarios [6][16]. - The new policies are designed to foster collaboration between supply and demand sides of the robotics industry, enhancing the integration of AI technologies into various sectors [1][6]. Event Highlights - A significant press conference was held on July 3, where over 300 participants, including media, enterprises, and investors, gathered to discuss the new policies, which were also broadcasted live, attracting over 30,000 viewers [2][4]. - The event marked a novel approach by bringing policy announcements directly to the business community, facilitating immediate engagement and feedback [22]. Financial Support Mechanisms - The policies include various financial support mechanisms such as "computing vouchers," "model vouchers," and "scenario vouchers," with a total of 30 million yuan allocated to assist qualifying AI enterprises [12][23]. - Specific measures include support for R&D expenses, with up to 1% of research costs reimbursed, and direct financial assistance for projects that meet certain criteria [20][24]. Industry Ecosystem Development - Huangpu District is committed to building a robust ecosystem for AI and robotics, focusing on data resource development, infrastructure enhancement, and the establishment of public service platforms [23][13]. - The district aims to create a favorable business environment that encourages innovation and attracts investment, thereby enhancing the overall competitiveness of its industrial sectors [24][22].

Core Viewpoint - The article discusses the evolution of large language models from "fast thinking" to "slow thinking" paradigms, emphasizing the importance of deep reasoning and logical coherence in AI development [2]. Group 1: Slow Thinking Technology - The new model DeepSeek-R1 enhances long reasoning chain capabilities through reinforcement learning, demonstrating superior understanding and decision-making in complex tasks [2]. - "Slow thinking" technology is identified as a key pathway for advancing large models towards higher intelligence levels, leading the industry towards greater automation and reliability [2]. Group 2: Seminar Details - A seminar titled "AI Slow Thinking: Complex Reasoning Technology of Large Models" was organized by Springer Nature, featuring Professor Zhao Xin from Renmin University of China, who shared insights on the latest research in slow thinking technology [2][6]. - Dr. Chang Lanlan, the Director of Computer Science Book Publishing at Springer Nature, discussed the new AI book resources and academic publishing in 2025 [2][6]. Group 3: Speaker Profiles - Professor Zhao Xin has a research focus on information retrieval and natural language processing, with over 200 published papers and significant contributions to large language models [8]. - Dr. Chang Lanlan has extensive experience in computer science book publishing and has been with Springer Nature for 14 years, overseeing AI-related publications [11]. Group 4: Book Recommendations - A new book led by Professor Zhao Xin and his team provides a systematic framework for learners in the large model field, aiming to help readers grasp core concepts and cutting-edge algorithms [19]. - The Springer Nature AI electronic book collection offers a comprehensive resource for research and learning, covering a wide range of topics from foundational knowledge to advanced research outcomes [21].

在工业制造的版图里，焊接是绕不开的关键一环，却常年被三大痛点牢牢困住：要靠大量人工 作业 、焊接质 量时好时坏、工人还得在高温、高辐射的恶劣环境里作业。 如今， 随着 机器人 自动化与人工智能技术的爆发式发展，焊接行业正迎来一场由技术创新驱动的大变革 ， 一家名为【 集萃智造 】的企业，正尝试 用一款会自己思考的焊接机器人，重新定义智能制造的边界。 ▍ 四次迭代，啃下卡脖子技术 据机器人大讲堂了解， 早在 2019 年，集萃智造就推出了第一代协作焊接机器人。此后五年里，他们没停下 升级的脚步，一路迭代四次，硬是造出了五款不同负载的机械臂，覆盖了从轻型到重型的多种焊接场景。 更关键的是，他们没走组装 厂 路线，而是 努力 把核心技术攥在了自己手里： 公司坚持核心零部件自主研 发， 硬件上 成功 攻克了直流无框电机、高精度双磁编码器等关键部件， 构建了其 机器人稳、准、快的基 础；软件上 ， 该公司还搞定了 机器人动力学建模、智能焊接工艺库，甚至搭起了跨平台操作系统 ， 相当于 给机器人装了最强大脑。 据悉，如今 现在，集萃智造的便携式协作焊接机器人已经扎根重工业：港口机械的大型构件、水利工程的金 属结构、钢结构 ...

在科研最前沿，实验场景对机器人的重复定位精度和系统扩展性提出近乎严苛的要求；而在商业展示领域，流 畅精准的动作、毫秒级的响应速度以及绝对安全的人机交互，已成为决定展示效果的关键。这些不断细化的期 待，共同构成了协作机器人必须跨越的技术门槛，也明确指向下一代产品的发展方向 ——它必须 更精准、更 可靠、更灵活 。 在机器人技术迅猛发展的浪潮中，我们正见证一场由应用端反向驱动的产业升级：科研与商用需求，正以前所 未有的标准 "倒逼"协作机器人向更高性能、更强耐用性和更广泛场景适配性的工业级水准迈进。 在此背景下，大象机器人推出了全新一代小型全谐波协作机械臂 ——myCobot Pro 450。该产品专为 教育科 研、实验验证与商业展示等对精度有极高要求的场景打造 ， 定位为一款可广泛应用于高精度操作任务的工业 级轻量化机械臂。 myCobot Pro 450在延续该系列轻巧设计的同时，实现了±0.1mm的重复定位精度与1kg的有效负载能力，并 具备超过10,000小时的设计使用寿命。它不仅显著降低了高精度操作任务的使用门槛，更成为贯穿具身智能 研究、自动化教学与轻工业应用的理想实践平台。 其卓越性能的背后，离不开 ...