2016年和2020年，诺力接连出手，先后并购了国内领先的智能物流系统集成商无锡中鼎和欧洲知名的物流 自动化解决方案提供商法国Savoye。 这两次关键并购，补全了诺力智能物流帝国的核心拼图，使其一跃成为 具备从智能叉车到大型智能物流集成系统全链条解决方案能力的全球化企业。 2024年，诺力股份的营收规模 已逼近70亿元人民币，其传统的叉车制造业务与高速增长的智能物流系统集成业务形成了"双轮驱动"、平分 秋色的均衡格局。 更值得关注的是，诺力股份子公司中鼎智能已于今年 5月9日正式向香港联交所递交上市申请，标志着公司智 能物流业务的发展进入了新的资本赋能阶段。 从数据来看，诺力的智能物流技术早已走出实验室，在全球范 围内完成了超过2000个物流系统工程案例 ，服务的客户名单星光熠熠，包括LG化学、宁德时代、国药集团 等横跨多领域的行业巨头。诺力的重载无人搬运车在钢铁、医药、食品、铜箔等高要求行业稳定运行多年，经 受住了复杂严苛工业环境的考验。随着技术的持续进步，诺力智能化无人叉车的竞争力日益增强，正不断渗透 到更广阔、更通用的市场场景中，展现出强劲的市场潜力。 ▍ 黄金赛道上的三重困境与技术野望 近期，国内仓 ...

Core Viewpoint - The article highlights the recent completion of a nearly 500 million yuan Series A financing round by "Star Motion Era," a developer of embodied intelligence and general humanoid robot technology, aimed at advancing humanoid robot technology and production [1] Group 1: Financing and Investment - The financing round was led by Dinghui VGC and Haier Capital, with participation from several well-known financial institutions and industry capital [1] - The funds will primarily be used to enhance the research and development of humanoid robot technology and to accelerate the "model-body-scene data" closed-loop operation [1] Group 2: Company Background - Star Motion Era, established in August 2023, is the only embodied intelligence company owned by Tsinghua University [1] - The company focuses on the frontier applications of general artificial intelligence (AGI) and aims to create humanoid robots that can empower various industries and households [1] Group 3: Core Team - The founder, Professor Chen Jianyu, has over ten years of experience in robotics and AI research, with numerous publications in top international conferences and journals [2] - The core team comprises members from prestigious institutions such as Tsinghua University, Peking University, and UC Berkeley, with over 80% of the team dedicated to research and development [4] Group 4: Product Development and Technology - Star Motion Era employs first-principles to address learning efficiency and task execution capabilities of robots [5] - The company has developed an "end-to-end embodied large model + self-developed humanoid body + full-body dexterous operation" triangular closed-loop system [5] Group 5: Technological Innovations - The company aims to enhance robots' environmental perception and autonomous decision-making capabilities, with breakthroughs in visual language action (VLA) models [7] - The ERA-42 model integrates visual perception, semantic understanding, and action prediction, capable of performing over a hundred tasks through voice commands [9] Group 6: Product Offerings and Market Presence - Star Motion Era has developed the Q5 wheeled humanoid robot, sharing core components with the STAR1 model to reduce development costs [11] - The company has established partnerships with leading firms like Haier Smart Home and Lenovo, particularly in logistics and retail service sectors [11]

Core Viewpoint - The 2025 RoBoLeague China Robot Football League marks a significant advancement in AI-driven robotics, showcasing humanoid robots that operate autonomously without remote control, relying on advanced visual sensors for environmental perception and decision-making [1]. Group 1: Overview of the Robot Football League - The league is the first 3V3 AI robot football competition in China and serves as a test event for the 2025 World Humanoid Robot Games [1]. - Humanoid robots utilize visual sensors to perceive their environment, enabling autonomous decision-making and interaction [1]. Group 2: Key Companies in Humanoid Robot Vision Technology Aobi Zhongguang - Founded in 2013, Aobi Zhongguang specializes in 3D visual perception products, including sensors and application devices, and has established strong customer loyalty in various industries [2][4]. - The company has developed a comprehensive 3D visual sensor system for robotics, integrating multiple technologies such as laser radar and structured light [4][5]. - Aobi Zhongguang aims to leverage advancements in AI and embodied intelligence to enhance its 3D visual perception technology across various applications [8]. Sutenju Chuang - Established in 2014, Sutenju Chuang focuses on AI-driven robotics technology, providing laser radar and perception solutions, and has partnered with over 2,800 global robot clients [9][11]. - The company launched the Active Camera product line, integrating depth, image, and motion information for enhanced robotic vision capabilities [9]. - Sutenju Chuang is committed to innovation in AI algorithms and hardware, aiming to solidify its position as a leading robotics technology platform [11]. Opto - Founded in 2006, Opto is a national high-tech enterprise specializing in machine vision core hardware and software products, with a focus on the robotics vision sector [12][13]. - The company is developing visual modules and solutions tailored for humanoid robots, leveraging its extensive experience in industrial robotics [13]. - Opto plans to enhance its product offerings through technological upgrades and innovations in AI and 3D processing [13]. Tianzhun Technology - Established in 2005, Tianzhun Technology is a global supplier of visual equipment, focusing on industrial applications and emphasizing AI-driven advancements [14][15]. - The company has developed a high-performance intelligent controller for humanoid robots, enhancing their perception and interaction capabilities [17]. - Tianzhun Technology aims to expand its presence in the humanoid robotics sector while advancing its machine vision technologies [18]. Crystal Optoelectronics - Founded in 2002, Crystal Optoelectronics specializes in precision optical components and has a strong presence in the optical industry [19][20]. - The company is adapting to the growing demand for optical hardware in robotics and drones, positioning itself for future growth [20]. - Crystal Optoelectronics is focused on developing high-end, intelligent, and customized optical solutions for emerging applications in humanoid robotics [22].

Core Viewpoint - The article discusses the development of a flexible multichannel muscle impedance sensor (FMEIS) by a research team from Nanyang Technological University, which addresses the limitations of traditional muscle monitoring tools and enhances human-machine interaction capabilities [2][4][24]. Group 1: FMEIS Development and Features - FMEIS is a flexible sensor with a thickness of only 220 μm and an elastic modulus of 212.8 kPa, closely matching human skin's elasticity [4][6]. - The sensor demonstrates high performance, achieving an accuracy of 98.49% in gesture classification and a determination coefficient (R²) of 0.98 in muscle strength prediction [4][10]. - Unlike traditional electromyography (EMG), FMEIS can detect impedance changes in deep muscle tissues, allowing for accurate readings even without significant body movements [4][10][17]. Group 2: Technical Specifications - The FMEIS system consists of a lightweight 4g sensor pad and a 53g control unit [6]. - The sensor pad utilizes a safe alternating current of 50 kHz and 0.4 mA for multi-channel signal injection and collection, ensuring stability during extensive movements [7]. - The design incorporates a modified polydimethylsiloxane substrate and conductive hydrogel electrodes, enhancing adhesion and signal quality over prolonged use [7][24]. Group 3: Performance Validation - FMEIS outperformed traditional EMG sensors in detecting both active and passive muscle movements, with a maximum detection depth of approximately 30 mm [17][24]. - In tests involving three participants, FMEIS achieved an average gesture classification accuracy of 98.49% and an average R² value of 0.98 for muscle strength regression, indicating strong robustness against variations in skin impedance and fat tissue thickness [16][24]. Group 4: Application Scenarios - FMEIS has shown potential in various applications, including human-robot collaboration, exoskeleton control, and virtual surgery [18][24]. - In human-robot collaboration, FMEIS enables natural interaction by interpreting muscle signals to drive robotic actions without visible hand movements, enhancing efficiency and safety [19][24]. - For exoskeleton control, FMEIS demonstrated a response delay of only 756 milliseconds, significantly improving grip strength by 65% during tests [21][24]. - In virtual surgery, FMEIS serves as a bridge between the operator and VR systems, allowing for precise feedback and control of surgical tools based on muscle force predictions [23][24].

Core Viewpoint - The rapid development of multi-modal large language models (MLLMs) is significantly enhancing video reasoning capabilities, driven by reinforcement learning (RL) as a key engine for this technological revolution [1] Group 1: TW-GRPO Framework Introduction - The TW-GRPO framework is proposed to address challenges in reasoning quality and reward granularity in video reasoning tasks, inspired by the traditional GRPO framework [2] - TW-GRPO integrates focused thinking and multi-level soft reward mechanisms for multi-choice QA tasks [3] Group 2: Key Improvements in TW-GRPO - The framework enhances information weighting and reward mechanism design, applying a soft reward mechanism from video localization to video reasoning tasks [4] - A dynamic weighting mechanism prioritizes high information density tokens, improving reasoning accuracy and efficiency by focusing on key content [4] - The multi-level reward mechanism redefines rewards, allowing for partial correctness in answers, thus improving training stability and efficiency [5] Group 3: Data Augmentation and Training Efficiency - TW-GRPO introduces a question-answer inversion (QAI) data augmentation technique to convert single-choice tasks into multi-choice formats, effectively expanding the training data pool [6] - This approach disrupts traditional equal treatment of tokens, enhancing training efficiency and reasoning performance through differentiated information processing [6] Group 4: Experimental Validation - Extensive experiments demonstrate TW-GRPO's effectiveness in video reasoning and general understanding tasks, outperforming Video-R1 by 18.8%, 1.8%, and 1.6% in various benchmarks [12][15] - The framework shows faster convergence and more stable learning processes compared to traditional GRPO, with shorter output sequences indicating more efficient reasoning [11][17] Group 5: Qualitative Analysis of Reasoning Paths - A qualitative comparison of reasoning paths between T-GRPO and TW-GRPO illustrates significant improvements in accuracy and efficiency in dynamic visual cue reasoning tasks [22]

东京大学的实验室里，一根覆盖着 淡黄色 皮肤的机器人手指正在缓缓弯曲。这不是硅胶，不是乳胶，而是 真正的活体人类皮肤组织 。更神奇的是，这层皮肤内部还有一套 "血管系统" 在源源不断地输送营养液。 7天 过去了，暴露在空气中的皮肤 依然保持着湿润和活性 。要知道，此前所有的活体皮肤机器人实验中，皮 肤组织在空气中几个小时就会干燥死亡。 这是不久前 发表在《 Advanced Intelligent Systems》 期刊 上的研究 。研究 团队通过模仿人体的血液循 环系统，为机器人皮肤设计了一套双层渗透性皮下支撑结构， 成功解决了活体组织机器人最大的技术难题 ——如何让皮肤在空气中长时间存活 。 "就像人体皮肤通过血管获得营养一样，我们的机器人皮肤也能通过内部的循环系统持续获得水分和营养。"研 究团队在论文中写道。这 或许 意味着，科幻电影中那些拥有真实触感的机器人，正在一步步成为现实。 ▍ 双层渗透性皮下支撑：机器人皮肤的 "血管系统" 研究团队的核心创新在于设计了一种 双层渗透性皮下支撑结构 。这个结构由两层组成：一层是密集穿孔的 3D打印骨架层，另一层是海绵状的聚乙烯醇（PVA）水凝胶层。 先说 骨架层 ...

Core Viewpoint - K-Scale Labs has launched the K-Bot, a low-cost open-source bipedal humanoid robot, emphasizing American manufacturing and aiming to democratize humanoid robotics through an open-source platform [1][23][36]. Group 1: Product Overview - The K-Bot is priced at $16,000 (approximately 114,644 RMB), with an early bird price of $8,999 (approximately 64,480 RMB) for the first 100 pre-orders, with shipments expected by November 2025 [1]. - K-Bot stands 1.4 meters tall, weighs 34 kg, and has a maximum payload of 10 kg, with a battery life of up to 4 hours [11]. - The robot features dual system architecture, utilizing reinforcement learning for movement control and a visual-language action strategy for advanced task execution [13]. Group 2: Company Background - K-Scale Labs, founded in early 2024 and headquartered in New York, has developed six humanoid robot models within a year, starting from a small garage-sized workspace [2][14]. - The company has received multiple rounds of funding, including $500,000 from Y Combinator and $4 million in seed funding, achieving a valuation of $50 million [14][16]. Group 3: Technical Features - The K-Bot is designed with open-source software and hardware, allowing users to access design documents and code [4]. - The upcoming product, Stompy, aims to lower the barrier to entry for robotics, featuring a fully open architecture that can be assembled using 3D-printed parts [6][8]. Group 4: Team Expertise - The team comprises engineers and robotics experts with experience from companies like Tesla and Meta AI, enhancing their capability in hardware and algorithm integration [18][22]. - The CTO, Paweł Budzianowski, has a PhD from Cambridge and has designed the core algorithm architecture for K-Bot, contributing to its advanced capabilities [21]. Group 5: Manufacturing and Supply Chain - K-Scale Labs emphasizes American manufacturing, although it sources components from Chinese suppliers, which has raised questions about the authenticity of its "Made in America" claim [23][27][33]. - The company collaborates with various Chinese firms for components, including Suzhou Maita Intelligent and Beijing Lingzu Times, to optimize costs and ensure production flexibility [22][33].

Core Insights - The medical robotics industry has entered a complex phase by 2025, with evolving product forms, clearer clinical pathways, and a more cautious capital environment [1] - The Third Global Surgical Robotics Conference focuses on the systematic upgrade of intelligent surgical systems, the construction of a full-chain ecosystem, and global pathways [1][2] Event Details - The conference will take place on September 5-6, 2025, at the Beijing Zhongguancun National Independent Innovation Demonstration Zone Exhibition and Trading Center [4] - The agenda includes a keynote session, a visit to a robot leasing hospital, and a gala dinner for marketing leaders [5][6] Key Topics - The conference will cover various topics, including the core architecture trends of next-generation surgical robots, AI integration, commercialization, hospital system implementation, global strategies, and supply chain innovations [7][8][9][11] - Discussions will also address challenges in integrating surgical robots with hospital systems, the construction of a global R&D system, and the commercialization pathways for surgical robots [12] Industry Impact - The event serves as a platform for hospitals, governments, and investors to redefine surgical environments and the future of healthcare [14] - For innovative companies, participation signifies recognition as industry leaders and an opportunity to share insights on the redefinition of medical systems through technology [17]

Group 1 - The main factors limiting the large-scale deployment of humanoid robots are their limited generalization capabilities and high manufacturing costs, with mainstream humanoid robots priced around 500,000 yuan, and some high-end models reaching over 1 million yuan [1] - High development costs, closed-source design architectures, and limited customization capabilities are major bottlenecks for rapid development in this field [2] - The Berkeley Humanoid Lite, developed by a team from the University of California, Berkeley, is a low-cost open-source humanoid robot that utilizes desktop 3D printing for manufacturing, with a BOM cost under $5,000, approximately 35,000 yuan [2][8] Group 2 - Current humanoid robot platforms are divided into commercial products and research prototypes, with commercial products like Agility Robotics' Digit and Fourier Intelligence's GR1 being too expensive for most research institutions and individuals [3] - Open-source hardware and software have significant potential in driving technological innovation and collaboration, allowing researchers to learn from each other and accelerate the iterative process [5] - The design of Berkeley Humanoid Lite emphasizes modularity and ease of manufacturing, with all structural components printable on standard desktop 3D printers, significantly reducing manufacturing complexity [9] Group 3 - Berkeley Humanoid Lite is designed as a medium-sized humanoid robot platform, standing at 0.8 meters tall and weighing 16 kilograms, featuring joint actuators and an integrated IMU for position sensing [10] - The robot's joint actuators utilize a 3D-printed cycloidal gearbox design, enhancing load capacity and lifespan through optimized gear parameters and manufacturing processes [12] - The robot's structure is built using aluminum extrusions for high rigidity and lightweight characteristics, with real-time monitoring of posture and motion state through the integrated IMU [14] Group 4 - The electronic system of Berkeley Humanoid Lite consists of a control computer, joint actuator controllers, an IMU, and a power management module, ensuring stable power supply and precise control [16] - Performance tests showed that the joint actuators maintained a mechanical efficiency of around 90%, indicating effective energy conversion and reduced energy loss [17] - The robot demonstrated excellent walking capabilities across various terrains, maintaining balance and adapting to slopes and steps, showcasing its dynamic balance control [20] Group 5 - Remote operation experiments validated the robot's precision and responsiveness in performing tasks like writing and object manipulation, indicating its suitability for various practical applications [21][24] - Berkeley Humanoid Lite represents an open-source, customizable, and cost-effective humanoid robot platform, significantly lowering development costs and simplifying manufacturing processes [25] - Future optimizations will focus on enhancing system stability and adaptability, further exploring the platform's potential application value [25]

Core Viewpoint - The article discusses the development and advantages of a new type of ankle rehabilitation robot that utilizes redundant drive mechanisms to enhance rehabilitation outcomes and address limitations of existing devices [1][2]. Group 1: Ankle Rehabilitation Robot Overview - The ankle joint is crucial for weight-bearing but is prone to injuries, making rehabilitation essential for recovery [1]. - Traditional rehabilitation methods are lengthy and inconsistent, leading to the exploration of robotic assistance for more effective and continuous treatment [1]. - Current ankle rehabilitation robots are categorized into platform-type and wearable-type, with platform-type robots being the primary choice for functional rehabilitation due to their ability to perform complex movements [1]. Group 2: Challenges in Existing Designs - Existing designs of ankle rehabilitation robots often have complex structures that are difficult to manufacture and assemble, impacting their effectiveness [1][2]. - Many traditional robots do not adequately consider the stretching movements necessary for rehabilitation, which can limit their effectiveness [1][2]. Group 3: New Robot Design and Features - The new ankle rehabilitation mechanism (PARM-N) and its redundant drive form (PARM-R) are designed to accurately simulate three core movements required for rehabilitation: dorsiflexion/plantarflexion, inversion/eversion, and axial traction [2]. - The design simplifies the structure while enhancing performance, reducing manufacturing costs and assembly complexity [3][5]. Group 4: Performance Analysis - The kinematic analysis of the mechanism is crucial for performance evaluation and optimization, with the study employing methods like the Newton-Raphson method for solving position equations [6][10]. - The redundant drive mechanism avoids singular configurations that can hinder rehabilitation effectiveness, while the non-redundant configuration has multiple singular forms [10][19]. Group 5: Stiffness and Optimization - Stiffness performance is analyzed using virtual spring methods, with results showing that the redundant drive mechanism exhibits better stiffness characteristics in specific ranges compared to the non-redundant design [22][29]. - A multi-objective size optimization approach is applied to enhance the overall performance of the mechanism, resulting in significant improvements in both motion/force transmission and stiffness metrics [28][29]. Group 6: Research Publication - The findings of this research have been published in the journal "Mechanism and Machine Theory," highlighting the contributions of the team from Nanjing University of Aeronautics and Astronautics [18].