九三 阅兵式上，中国机器狼正式亮相 ，吸引了全世界的目光。 这是 建国以来仿生智能机器人首次接受检阅 ， 机器狼 作为陆上无人作战方队的重要组成部分，向世界展示了中国军队在智能化建设上的最新成果和未来方向。 此次机器狼接受检阅，是 新域新质作战 力量发展的关键里程碑 ，对整个仿生智能机器人行业都将产生极大的振奋作用。 央视军事、央视新闻、人民日报、新华社等 各大主流媒体聚焦 报道 ， 特别强调此次亮相的是 "机器狼"，而非 "机器狗"。 ▍ "机器狼"的战斗力更强 相较于 "机器狗" ， "机器狼" 的作战能力更强。 有军事专家此前称，在城市巷战中， "机器狼"作战群 可以 进入人类难以进入的狭小区域 。这样，它们就能在危险区域执行任务， 与人类形成有效的配合， 这在 未来战场上将非常有用。 一支完整的"机器狼"作战群由1辆载车或控制车，结合若干多型四足机器狗组成，可实现 "人—车—狼"互联互通、信息共享和动态自主协同 。基于场景应用需 求，Q20A可根据任务形式来灵活配置模块化上装载荷，并 与其它无人装备、系统等进行灵活编组，协同作战。 今年 8月初央视播出的思想解读类融媒体片《攻坚——矢志强军向一流》中， ...

中航迈特专注金属 3D打印设备与材料技术创新与产品服务，聚焦具身智能机器人行业，可为该行业提供从原 型开发到批量生产的金属3D打印装备及金属粉末材料支持。 中航迈特将在 2025年TCT深圳展3C12展位 上，为机器人行业创新带来金属3D打印智造解决方案。 展会上，中航迈特将携 MT170H、MT280及MT400M金属3D打印机 等闪耀现场，并将于现场演示机器人身 体部件的一体化成形打印。系列设备满足多样化机器人制造需求，支持复杂结构一体化、产线化批量生产，为 机器人制造企业提供可靠的生产解决方案，让具身体制造更自由、更高效。 小指如章鱼般柔软蜿蜒，指尖还自带 "透视眼"，整只"手"遇热能变色提醒，单根"手指"成本不到1块钱。 在 2025世界人工智能大会（WAIC）上， 复旦大学智能机器人与先进制造创新学院智能机器人研究院甘中学 教授团队自主研发的多源仿生 3D打印柔性灵巧手首次亮相 。 这款灵巧手打破传统设计，融合仿生结构、 3D打印柔性材料与传感控制系统，不仅能灵活抓握、夹取、勾 挑，还具备温度变色、视觉识别等功能，巧妙应对复杂环境，同时大幅降低制作成本，诠释机器人与人类共融 共生的美好未来。 深圳， ...

Core Insights - The article discusses the challenges in robotic manipulation and introduces a new system called HIL-SERL that significantly improves the efficiency and effectiveness of robotic training in real-world scenarios [1][2]. Traditional Methods Challenges - Traditional robotic control methods require extensive engineering design or imitation learning, which often lack adaptability and struggle in new environments [1]. - These methods fail to achieve human-level proficiency and speed, leading to inefficiencies in real-world applications [1]. HIL-SERL System Overview - The HIL-SERL system developed by a research team at UC Berkeley allows robots to learn complex tasks with only 1 to 2.5 hours of real-world training, achieving near-perfect success rates and surpassing human execution speeds [2][3]. - The system combines human guidance with autonomous exploration, creating an efficient and safe learning loop [3]. System Architecture - HIL-SERL consists of three core components: an executor process, a learner process, and a replay buffer integrated within the learner [4]. - It employs off-policy reinforcement learning techniques to optimize behavior strategies by leveraging historical data, allowing robots to learn from human demonstrations and assess the contribution of different actions towards achieving goals [4]. Performance in Multi-Task Scenarios - The system was tested on challenging tasks such as precision assembly, dual-arm coordination, and dynamic manipulation, demonstrating its versatility [5][8]. - In precision assembly tasks, robots achieved sub-millimeter accuracy, while in dual-arm coordination tasks, they effectively managed complex operations requiring synchronized movements [8]. Results and Adaptability - After 1 to 2.5 hours of training, robots achieved nearly 100% success rates and executed tasks 1.8 times faster than traditional imitation learning methods, which had an average success rate of 49.7% [9]. - The robots exhibited remarkable adaptability, successfully adjusting to unexpected situations, such as misalignments or disturbances, showcasing their ability to learn from real-time feedback [12]. Learning Mechanism - HIL-SERL's adaptability stems from its ability to evolve different control strategies based on task requirements, allowing for real-time adjustments and corrections [13][16]. - For high-precision tasks, the system employs a closed-loop response strategy, while for dynamic tasks, it utilizes an open-loop predictive strategy, demonstrating a high level of confidence in executing planned actions [13]. Conclusion - The research highlights the potential of HIL-SERL to overcome traditional reinforcement learning limitations, enabling efficient learning of complex skills in real-world environments [14]. - This advancement opens new avenues for industrial applications, particularly in flexible manufacturing sectors requiring small-batch production [14].

Core Viewpoint - The article highlights the cybersecurity vulnerabilities faced by robotic companies, particularly in the context of Pudu Robotics, as they expand globally and integrate into sensitive environments like restaurants and hospitals [1][11]. Summary by Sections Security Vulnerabilities - Bobdahacker exposed significant security flaws in McDonald's ordering system and Pudu's AI chatbot, revealing that simple passwords like "123456" could be exploited to gain unauthorized access [1][3]. - The vulnerabilities allow attackers to control food delivery robots, redirect orders, and disrupt restaurant operations through DDoS attacks [3][5]. Attack Capabilities - Attackers can view call history for any robot and retrieve up to 20,000 store IDs in a single request [5]. - They can initiate, cancel, or reschedule tasks for any robot globally [6]. - Modifications to robot settings, including nicknames and operational behaviors, are also possible [7]. Broader Implications - The security risks extend beyond restaurant chaos, potentially affecting hospitals relying on robots for medication delivery, leading to treatment delays or misdelivery [8]. - Pudu Robotics, the largest commercial service robot manufacturer, faced scrutiny after failing to respond promptly to vulnerability reports, only taking action after warnings from major clients [9][10]. Industry Challenges - The article emphasizes that many robotic companies lack basic security measures, such as dedicated security contacts and authenticated API controls, often only responding to threats when reputational damage is imminent [12]. - As automation plays a larger role in critical operations, the need for robust security capabilities that match technological innovations becomes increasingly urgent [12].

2025年9月5 -6 日，MedRobot与机器人大讲堂联合主办的 第三届全球手术机器人大会（Global Medical Robotics Conference 2025） 在北京中关村展示中心举行，吸引了来自医院、企业、科研院所、投资机构 的600余位专业嘉宾，共同探讨手术机器人领域的技术演进、临床应用与全球化趋势。作为大会的核心环节之 一， "2025全球 医疗 机器人系列大奖" 正式揭晓。 由专家委员会评定的 "年度手术机器人、技术创新奖、临床创新奖、市场表现奖、国际拓展奖、卓越服务伙伴 奖、卓越供应链奖、 年度康复机器人 " 八大奖项，集中表彰过去一年在自主研发、核心部件攻关、国际认证 落地与商业模式探索方面取得突出成果的企业代表。 与往年不同的是，本届奖项特别设置 "年度手术机器人企业家、年度手术机器人医生奖、创新转化奖" 三项个 人类奖项，聚焦那些在一线推动行业进步的关键人物。从医生到工程师，从科研到转化，从战略决策到一线操 作，他们代表着机器人技术落地过程中的关键推动力。 从腔镜到骨科，从穿刺导航到智能远程，从国产突围到全球化布局，这些企业正构成当下医疗机器人产业格局 中最具活力的技术力量， ...

Group 1 - The "Machine Wolf" made its debut at the 93rd National Day Parade, showcasing enhanced capabilities in reconnaissance, strike, and support compared to the "Machine Dog" [3] - The "Machine Wolf" is designed for collaborative operations between manned and unmanned vehicles, marking a new breakthrough in land-based combat [3] - The "Machine Wolf" features roles such as reconnaissance, precise strike, and support, demonstrating tactical coordination with soldiers [3] Group 2 - OpenAI plans to collaborate with Broadcom to mass-produce its self-developed AI chips, aiming to reduce reliance on Nvidia, which currently holds about 80% of the AI chip market [7] - The first chip from OpenAI focuses on AI model training, with future plans for more powerful processors, although self-developed chips present significant challenges [7] - OpenAI's chip design team has expanded to at least 40 members, but remains smaller compared to teams at companies like Google [7] Group 3 - Shoucheng Holdings announced the establishment of a "Robot Advanced Materials Industry Company" to focus on key material research and industrialization for the robotics industry [10] - The new company will concentrate on core materials such as electronic skin and lightweight PEEK, aiming to enhance robot performance and cost control [10] - Shoucheng Holdings is building a complete industrial chain, with the new company serving as a key component in upstream development [10] Group 4 - Stone Technology launched three new robotic lawn mowers at IFA 2025, entering the robotic lawn mower market with models featuring advanced capabilities [13] - The RockMow Z1 boasts all-wheel drive and can handle steep slopes, while the S1 offers AI smart mapping [13] - The company also introduced various new products, including advanced vacuum cleaners, although some pricing and release dates remain undisclosed [13] Group 5 - Aiper unveiled its first AI pool cleaning robot, Scuba V3, at IFA Berlin 2025, which is noted for being the lightest visual recognition AI pool cleaner [15] - Scuba V3 features waterline detection and can intelligently avoid obstacles, achieving a 40% reduction in operational time and energy consumption [15] - The robot is set to be released in early 2026 at a price of $1199 [15]

Core Viewpoint - The article emphasizes the importance of high-precision, high-flexibility, and high-speed automation in key industries such as precision manufacturing and electronic assembly, highlighting the advancements in vibration suppression technology that enhance production capacity and product quality while reducing material waste [1][5]. Vibration Suppression Technology - The new vibration suppression algorithm significantly reduces vibration during the startup phase, optimizing vibration amplitude by up to 90% across XYZ axes [2]. - Positioning error during the stopping phase is improved by up to 96%, eliminating the need for secondary adjustments [2]. - Stability under different loads is enhanced, with vibration amplitude fluctuations reduced to ≤5% when switching between 5kg and 10kg loads, compared to over 40% previously [2][8]. - The response speed from static to stable state is improved by 16% [2]. Zero Scrap Assembly Practices - In precision assembly scenarios, especially for fragile electronic components, the stability of robot startup and stopping is critical, with requirements reaching the micron level [3][5]. - The optimized vibration suppression technology achieves a maximum vibration amplitude of around 0.05mm during startup and maintains a positioning error of less than 0.02mm, thus preventing economic losses and improving product qualification rates [5]. Flexible Efficiency in Manufacturing - The article discusses the shift from rigid efficiency to flexible efficiency in industrial manufacturing, particularly in sectors like automotive electronics and medical devices, where production characteristics include small batches and diverse products [6]. - The new technology allows for better responsiveness to load changes, maintaining production efficiency and product quality, particularly in mixed-line production scenarios [6][8]. Quality and Efficiency Synergy - The implementation of vibration suppression technology leads to faster response times in visual-assisted assembly processes, increasing hourly production capacity by approximately 10% without changing camera parameters [9][11]. - The reduction in vibration enhances the stability of camera operations, improving image clarity and precision in component alignment, thus decreasing rework rates caused by poor visual recognition [11]. Future Outlook - The company aims to continue innovating in the field of general-purpose intelligent robotics, focusing on customer needs and leveraging breakthrough technologies to create competitive advantages in quality, ultimately supporting the upgrade of industrial manufacturing [11].

Core Viewpoint - The article discusses the emerging trend of humanoid robots entering households, highlighting both the excitement and skepticism surrounding this development. It emphasizes the need for a more user-centered approach in the design and deployment of household robots, as current trends may not align with user preferences and safety concerns [1][12]. Group 1: Expert Insights - Maya Cakmak, a professor at the University of Washington, expresses her concerns about the practicality of humanoid robots in homes, suggesting that non-humanoid robots may be more effective and user-friendly [3][4]. - Cakmak's research indicates a significant preference among users for specialized robots over humanoid ones, citing safety, privacy, and space concerns as primary reasons [6][8]. Group 2: User Preferences - A study involving 76 participants revealed that most preferred specialized robots for household tasks, associating humanoid robots with safety risks and privacy issues [6][7]. - Users expressed that specialized robots are perceived as safer, more private, and less intrusive compared to humanoid robots, which are often seen as bulky and unnecessary [6][11]. Group 3: Practicality vs. Design - The article highlights a critical contradiction in the industry: while companies pursue humanoid robots for their versatility, users may not require human-like features for effective assistance in household tasks [9][11]. - Cakmak argues that simpler, non-humanoid designs could fulfill most household needs without the complications associated with humanoid robots [11][12]. Group 4: Future Directions - The article concludes that the industry should focus more on user insights and practical applications rather than merely technological advancements. It suggests that companies should share user research data to better align product development with actual user needs [12][14].

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].