Core Viewpoint - The article discusses the upcoming humanoid robot combat event organized by Yushu Technology, highlighting the significance of humanoid robots in the context of AI and advanced manufacturing, particularly focusing on the role of planetary roller screws as a core component in humanoid robots [1][3]. Summary by Sections Humanoid Robot Combat Event - Yushu Technology has announced the first humanoid robot combat event scheduled for May 25 in Hangzhou, featuring both performance and competitive segments [1]. - The performance segment will showcase humanoid robots engaging in traditional combat sports, while the competitive segment will involve four teams competing in real-time controlled matches [1]. Importance of Planetary Roller Screws - Planetary roller screws are identified as critical components that convert rotational motion into linear motion, affecting the precision and stability of humanoid robots [1]. - The main types of screws include trapezoidal screws, ball screws, and planetary roller screws, with the latter being favored for its high efficiency, precision, longevity, and load capacity, despite higher manufacturing costs [1][3]. Market Dynamics and Domestic Opportunities - The planetary roller screw market is currently dominated by foreign brands due to their technological lead and established customer bases, as domestic companies began developing these products only in the 1990s [3]. - Domestic leading companies are actively entering the planetary roller screw market, aiming for domestic substitution and growth opportunities in the humanoid robot sector [3]. Company Profiles Beite Technology - Established in 2002, Beite Technology specializes in automotive components and has a strong technical foundation in the industry [4][5]. - The company is expanding into the humanoid robot sector, leveraging its existing technology and product offerings to develop various screw products, including planetary roller screws [7][8]. Wuzhou Xinchun - Founded in 1999, Wuzhou Xinchun has over 20 years of experience in precision manufacturing and is transitioning into high-end applications, including components for humanoid robots [9][11]. - The company has developed several new products, including reverse planetary roller screws, and is focusing on high-end precision screw business as a new growth avenue [11]. Shuanglin Co., Ltd. - Established in 2000, Shuanglin Co., Ltd. specializes in automotive parts and is expanding into humanoid robot components, particularly reverse planetary roller screws [12][14]. - The company has successfully developed prototypes for humanoid robot joints and is establishing production lines for planetary roller screws [12][14]. Best - Founded in 1997, Best focuses on precision components and has extended its business into humanoid robots, developing high-precision screw products [15][17]. - The company is leveraging its subsidiary, Yuhua Precision Machinery, to enhance its technological capabilities in the screw industry [17]. Hengli Hydraulic - Established in 2005, Hengli Hydraulic specializes in hydraulic components and is advancing into linear actuators, including planetary roller screws [18][20]. - The company is investing significantly in R&D for its linear actuator project, which includes various screw products [20]. Slin Co., Ltd. - Founded in 2004, Slin Co., Ltd. is focusing on automotive bearings and is now entering the robot components market, particularly in harmonic reducers and actuators [21][23]. - The company is developing production lines for robot components, including planetary roller screws, to capitalize on the growing market [23].

Core Viewpoint - The article emphasizes the critical role of force sensors in humanoid robots, highlighting their function as the "nerve endings" for perception and interaction, enabling robots to convert tactile sensations into quantifiable data streams [1]. Industry Overview - The humanoid robot industry is experiencing explosive growth, with approximately 160 global humanoid robot companies, over 40% of which are based in China [3]. - In 2024, the number of new humanoid robot releases is expected to exceed 106, with China accounting for 86 of these, representing 81% of the global total [3]. Market Dynamics - The demand for force sensors is surging, driven by the increasing commercialization and R&D investments in humanoid robots [4]. - The cost of sensors in the humanoid robot industry constitutes 25.8% of the total cost, making it a key area for technological breakthroughs and cost reduction [3]. Company Spotlight: Blue Dot Touch - Blue Dot Touch has rapidly ascended to the forefront of the six-dimensional force sensor market since entering in 2019, achieving significant growth in shipment volumes and surpassing European and American brands in core performance metrics [6]. - The company has established partnerships with leading humanoid robot manufacturers, including Xiaomi and UBTECH, and is also penetrating high-value sectors such as new energy vehicles and healthcare [7]. Product Development - Blue Dot Touch launched two new six-dimensional force sensors, LA35 and LA77, designed for compact applications and high-load capabilities, respectively [11][13]. - The company also introduced two models of joint torque sensors, LC and LD, which are compact and lightweight, enhancing robot performance without increasing joint size [14][16][18]. Market Position and Future Outlook - Blue Dot Touch is projected to capture 70%-80% of the market share for six-dimensional force sensors in humanoid robots in 2024 [9]. - The company has a dedicated R&D team of nearly 100 people focused on customizable sensor solutions, positioning itself as a core supplier in the global market [19]. - The company has achieved over 100% growth in cash flow, profit levels, and revenue growth in 2024, indicating strong operational performance [20].

随着人工智能技术的不断发展，机器人在执行日常任务中的能力也在逐渐提升。如何让机器人执行像人类一样 爬楼梯、坐下和站立等复杂动作，成为机器人学领域的重要课题。传统的机器人控制方法通常依赖于精确的物 理模型、手工设计的奖励函数或运动捕捉类数据来训练机器人。然而，这些方法往往需要大量的人工干预和特 定环境的设计，难以适应真实世界中多变的情境。 首先，系统从单目视频中提取人体的三维关节位置，并利用结构光或深度学习方法重建周围环境。通过联合优 化技术，将人体的三维运动轨迹与环境几何信息恢复至全局坐标系中，确保二者的一致性，以适配不同的仿真 与物理引擎。 接着，系统将重建的运动数据与环境信息转化为类人机器人可执行的动作，并在仿真环境中进行训练。通过采 用DeepMimic风格的强化学习方法，机器人学习如何在不同环境中模仿人类视频中的动作。 近日UC Berkeley大学研究人员提出了一套名为VideoMimic的框架，该框架通过日常生活中的视频，自动生 成类人机器人的控制策略。该方法不需要依赖复杂的传感器数据或手工设计的奖励函数，而是通过观看普通的 单目视频（如智能手机拍摄的日常视频），将视频中的人类动作和环境信息转化为 ...

Core Viewpoint - The article discusses the importance of accurate human intent estimation and effective role allocation in physical human-robot collaboration (pHRC), highlighting the limitations of current methods and introducing a new framework called DTRT to address these challenges [1][5][14]. Group 1: Challenges in pHRC - Achieving seamless collaboration between robots and humans requires effective strategies for accurate human intent estimation and dynamic adjustment of robot behavior [5]. - Current intent estimation methods primarily rely on short-term motion data, which limits their ability to predict long-term changes in human intent [1][5]. - The inability to anticipate changes in human intent can lead to potential conflicts and negatively impact the safety and efficiency of collaborative tasks [1][5]. Group 2: Introduction of DTRT Framework - The DTRT framework, developed by researchers from the Chinese Academy of Sciences and CASBOT, utilizes a dual transformer-based approach to enhance human intent estimation and role allocation in pHRC [1][2]. - DTRT addresses the core issues of inaccurate intent estimation and inflexible role switching by employing a hierarchical structure that captures human intent changes through guided motion and force data [2][7]. Group 3: Key Features of DTRT - DTRT tightly integrates human intent estimation with role allocation, allowing for quick detection of intent changes and timely adjustments to reduce human-robot discrepancies [7][8]. - The framework's human intent estimation module processes both motion and force data, improving the accuracy of intent predictions [8]. - The role allocation mechanism based on differential cooperative game theory ensures that robot behavior aligns closely with human intent while maintaining the robot's autonomy [8][7]. Group 4: Experimental Validation - A series of comparative experiments were conducted to validate the effectiveness of the DTRT framework, demonstrating significant advantages in prediction accuracy and collaborative performance [9][10]. - Key performance indicators showed that under the DTRT framework, the average human-robot collaboration angle reached 76.4°, with a robot assistance level index of 1.5, and the system maintained a good collaboration state 61.8% of the time [10][13]. Group 5: Future Implications - The introduction of DTRT represents not only an algorithmic breakthrough but also an attempt to reconstruct human-robot relationships, providing a versatile and valuable technical pathway for the development of humanoid robots [14]. - The research approach and core mechanisms of DTRT are expected to be further expanded and deepened in various practical applications, including industrial manufacturing and complex operations [14].

美国 CES 2025 上，一台搭载仿生多关节机械手的追觅扫地机器人正演示着令人惊叹的场景：它绕过散落的 玩具，夹起地板上的袜子放入收纳篮，随后调整拖布湿度对咖啡渍进行重点清洁。 这一系列动作的背后，是 " 具身智能 " （ Embodied Intelligence ）技术从实验室走向家庭的标志性突破 ： 扫地机器人不再仅仅是清洁工具，而是进化成具备感知、决策与执行能力的 " 家庭智能体 " 。 这一变革的驱动力，源自英伟达创始人黄仁勋 2023 年的预言： " 具身智能将成为人工智能下一波浪潮。 " 当 ChatGPT 掀起语言模型的狂潮后， AI 与物理世界的深度融合成为新的竞技场。 而在这一领域，扫地机器人意外成为先锋：科沃斯、石头、追觅、云鲸四巨头占据全球 75% 市场份额，它们 正将 " 具身智能 " 从学术概念转化为消费级产品。 这场革命的核心逻辑在于：当 AI " 大脑 " 与机械 " 身体 " 深度协同，硬件便能突破功能边界，从单一任务 执行者升级为环境自适应者。 而扫地机器人，凭借成熟的产业链、庞大的用户基数和清晰的场景需求， 正 成为具身智能落地的最佳试验 田。 ▍ 从 " 工具 " 到 ...

近日来自美国旧金山的Foundation Robotics Labs在X平台上更新了Phantom人形机器人的最新研究进展。视频 中Phantom在模拟物流场景下对快递包装进行分拣，从画面来看，该视频未进行加速处理，这也是Phantom人 形机器人首次展示抓取匀速移动物品的能力。 官方负责人表示，Phantom机器人已经在汽车制造生产线进行部署，接下来会针对消费品制造的两家合作客 户进行定制化开发方案。 ▍ 模块化设计 Phantom更注重上半身作业能力 Phantom 人形机器人身高 1.75 米，体重 80 公斤，有效载荷能力 20 公斤。上半身作为核心操作部分，集成 计算单元、摄像头、电池和传感器，高度 0.75 米，重 37.5 公斤，拥有 19 个自由度。下半身负责移动和平 衡，配备执行器和平衡系统，高度 1 米，重 42 公斤，行走速度 1.7 米/秒，可在复杂地形保持稳定行走。 Phantom 采用 Foundation Robotics Labs 的自研旋转致动器，该技术结合了液压和电动致动器的部分优 势，使机器人结构紧凑且运行安静。其最大峰值扭矩为 160 牛米，最大后驱动扭矩小于 1.0 ...

陕西遭遇 60年一遇的极端特大干旱 全国多地出现极端大风天气 ， 50年树龄的老树都被连根拔起 北方 多 地在冰雹突袭后 ， 气温突破五月历史 最高记录 ，河南部分站点地表温度甚至高达 70℃。 今年以来，极端天气在我国多地肆虐 ， 而纵观全球，亦是 极端天气事件 频频发生 。 这些异常现象不禁让人追问：地球气候系统究竟发生了什么？ 众多研究表明，森林面积的急剧减少可能是 气候变化的 重要诱因之一。以今年为例，美国新泽西州南部的野 火在 短短 三天内烧毁超过 12,500英亩森林，韩国庆尚道发生的大型山林火灾，更是烧毁3.6万公顷山林。 森林，尤其是树冠层，堪称环境保卫战里的 "扫地僧" ， 是 地球生态系统的关键组成部分， 其不仅是地球 的天然空调，调节着区域气候， 更蕴藏着丰富的生物 ， 是维持生物多样性的重要场所。 然而，由于树冠位置较高，一直以来，科学家对其进行研究困难重重，可谓 "难于上青天"。 无论树干是笔直挺拔，还是弯曲盘绕，表面是粗糙的树皮，亦或是相对光滑的区域， Monkee都能凭借这些 粘附点轻松应对。而更令人意想不到的是，这一秘密武器的核心竟然是螺丝。 螺丝实现爬树功能需要经过两个关键步 ...

Group 1 - Tesla released a new video showcasing the humanoid robot "Optimus" performing various tasks such as throwing garbage, cooking, vacuuming, and ironing, all accomplished through a single neural network that learns directly from human videos [1] - Elon Musk reiterated that the Optimus humanoid robot will become one of Tesla's most important products during a recent interview [1] Group 2 - Apple introduced a new training method called "PH2D" that combines human coaches and robot demonstrators to enhance the efficiency and cost-effectiveness of humanoid robot training, addressing the limitations of traditional methods [2][4] - The new approach utilizes modified consumer-grade devices to create training materials, and Apple developed a model named "Human-humanoid Action Transformer" (HAT) to process data from both human coaches and robot demonstrators [4] Group 3 - A collaboration between Harbin Institute of Technology and Dongfang Electric Group achieved significant advancements in soft robotics, developing a smart soft robot based on MXene materials for special environments [5][7] - The research emphasizes the integration of material innovation and structural design, providing new technical means for the development of three-dimensional soft robots [7] Group 4 - French retail giant Leclerc's president called for robots and AI industries to contribute to social security funding, suggesting a "robot tax" to address the financial strain on the social security system caused by technological advancements [8][10] - The proposal aims to create new funding sources and improve employee wage levels in response to the increasing pressure on social security funds [10] Group 5 - Researchers from Sungkyunkwan University reported a new type of artificial synaptic mechanical receptor array that significantly reduces data burden while improving recognition accuracy in tactile sensing applications [11][13] - The study demonstrated that the new method achieved a peak accuracy of 92.3% in texture recognition tasks using only 10.6% of the data [13]

扫描上方二维码 ，免费预约观赛 名额！亲眼见证科幻电影中的场景在现实中如何实现！ 【史上最燃国际人形机器人技能大赛：顶级看点揭秘！】 人形机器人首次真实场景"极限闯关"、全球高标准人形机器人足球3V3巅峰对决、人形机器人核心技术全球首次 大规模集中亮相、 顶尖机器人超能力达人秀震撼上演、全自主智能赛道AI极限对决、从工业应用到商业应用到 走进千家万户全面覆盖未来生活场景、优胜项目 直通"千台购置计划"、院士领军专家超强阵容亲临评审、国内 首个仿真+实体双线赛道赋能算法落地、多队争夺千万级产业化订单…… 【震撼规模：28大真实场景×5大专业赛道×顶尖战队同台竞技！】 这是一场 技能链条最完整、技术最全面、行业最前沿、应用领域最广的具有国际影响力的大赛！ 全球媒体争相 报道，预计将吸引上千名行业精英现场观摩！代表着 具身智能领域最高技术水平和场景技能能力 的巅峰展示！ 【开源引领：打造全球最大具身智能开放式生态系统】 本次大赛从 开源生态、场景驱动、技术创新、竞技互动 四个层面，搭建全球最开放的人形机器人技术交流平 台，推动 高负载技术、地面适应技术、双臂协作能力、算法泛化能力等研发突破，向工业、家庭、商用和特种 ...

Core Viewpoint - The emergence of humanoid robots presents a new ecosystem and significant opportunities for investment in chip architecture and hardware development, suggesting that companies that align with this trend may capture early advantages in the market [1]. Group 1: Competition and Challenges - The "brain" of humanoid robots typically refers to the main control chip or computing platform, while the "cerebellum" is responsible for motion control, with high-performance chips being the preferred choice [2]. - Major players in the chip market for humanoid robots include NVIDIA's Jetson, Intel's x86 chips, and others like Allwinner Technology and Rockchip [2]. - Companies like Tesla, UBTECH, and others are utilizing various Intel and NVIDIA chips for their humanoid robots, highlighting the competitive landscape in chip selection [3]. Group 2: NVIDIA's Developments - NVIDIA is not just a chip provider but is also leading advancements in robotics and AI, with its CEO emphasizing the role of physical AI and robotics in the next industrial revolution [4]. - The launch of NVIDIA's Isaac GR00T N1.5 cloud-to-robot computing platform aims to enhance the adaptability of robots in various environments and improve task success rates [4]. - NVIDIA's collaboration with numerous robotics companies, such as Foxlink and Agility Robotics, showcases its influence in accelerating robot development through simulation and training tools [6]. Group 3: Huawei's Robotics Chip Ecosystem - Huawei's Ascend 910D AI chip, expected to be a significant player in the market, boasts impressive specifications, including a theoretical peak performance of 1.2 PFLOP/s, surpassing NVIDIA's H100 [9]. - The U.S. Department of Commerce has imposed strict export controls on Huawei's Ascend AI chips, complicating its market position [10]. - Huawei's strategy focuses on technology empowerment and ecosystem collaboration, aiming to establish itself as a foundational technology standard setter in the robotics field [12]. Group 4: Business Models and Strategies - Huawei adopts a "sell the shovel" model, emphasizing ecosystem collaboration over direct hardware manufacturing, which allows for risk mitigation and resource sharing among partners [13]. - The company aims to create a closed-loop system that integrates demand, data accumulation, and technology iteration, particularly in industrial applications [16]. - In contrast, NVIDIA focuses on building a comprehensive technology stack that lowers industry entry barriers, while Huawei emphasizes vertical integration in specific scenarios [15].