Core Viewpoint - Liang Wenfeng has been recognized as one of the top ten scientists of 2025 by the journal Nature for his significant contributions to the AI field through the DeepSeek model, which has disrupted traditional AI paradigms [3][4][8]. Group 1: DeepSeek Model and Its Impact - The DeepSeek model has dramatically reduced costs in the AI industry while enhancing the global visibility of domestic large models [9]. - DeepSeek demonstrates that high-performance models do not necessarily require vast amounts of data, parameters, or servers to achieve top-tier capabilities [10]. - The recent release of the DeepSeek V3.2 series model has achieved the highest evaluation level among current open-source models in the Agent domain [11][12]. Group 2: Liang Wenfeng's Background - Liang Wenfeng, born in 1985, excelled academically, becoming a top student in his high school and later studying electronic information engineering at Zhejiang University [14][15]. - He transitioned into quantitative investment in 2008, capitalizing on the emerging trend of quantitative trading in China, and his team quickly grew their proprietary funds to over 500 million yuan [17]. - In 2021, his firm, Huanfang Quantitative, became one of the largest quantitative private equity firms in China, managing over 100 billion yuan [19]. Group 3: Recognition of Other Researchers - Mengran Du, another researcher recognized by Nature, discovered the deepest known animal ecosystem on Earth, contributing to the understanding of extreme life and carbon cycling in deep-sea environments [25][28]. - Du's research has been published in prestigious journals and she has participated in numerous deep-sea explorations, enhancing the scientific community's knowledge of deep-sea ecosystems [33].

Core Insights - The article discusses the current state of mass production of embodied robots, highlighting the commitments and developments from various companies in the industry [1][5]. Group 1: Company Developments - Hyundai Motor has committed to deploying tens of thousands of Atlas robots in its manufacturing and logistics operations, addressing production capacity challenges by integrating automotive manufacturing expertise to support Boston Dynamics in scaling robot production [2][4]. - UTree Technology has not disclosed specific order volumes for the year but anticipates annual revenue exceeding 1.2 billion [7]. - ZhiYuan Robotics announced the cumulative production of 5,000 robots, with applications across entertainment, manufacturing, logistics, and scientific research [8]. - UBTECH Robotics secured a significant order worth 264 million yuan for its Walker S2 robot, which can autonomously change batteries, and has established contracts for various industrial applications [10][11]. - Tesla's Optimus robot is positioned as a core future asset, with a target of producing 5,000 units by the end of December 2025 and scaling up to 100,000 units by the end of 2026 [14]. Group 2: Order and Production Capacity - UBTECH's Walker series has achieved a cumulative order volume of 1.3 billion yuan, with a production capacity of 300 units per month, expecting to exceed 500 units in deliveries by 2025 [12]. - The partnership between Shenzhen Huizhi and ZhiPing aims to deploy over 1,000 embodied intelligent robots in logistics and manufacturing processes over three years [15]. - Star Dust Intelligent announced a strategic cooperation for a thousand-unit order of humanoid robots, focusing on industrial applications and leveraging AI technology for enhanced operational capabilities [17][19]. - Songyan Power expects to surpass 2,500 units in orders for bio-inspired and educational robots, with total order value exceeding 100 million yuan [20]. - Original Force Unlimited signed a strategic cooperation agreement worth 260 million yuan with a cultural tourism group [22]. Group 3: Market Trends and Future Outlook - The article indicates a growing trend in the deployment of humanoid robots across various sectors, including industrial, manufacturing, and logistics, with expectations for expansion into more niche markets such as 3C and automotive [19]. - The capital market performance of companies like Zhongqing Robotics shows significant investment interest, with plans to deliver 2,000 units over three years and collaborations with major firms like JD and NVIDIA [24]. - Leju Robotics has ramped up its delivery pace from hundreds to nearly a thousand units, with a target of 2,000 units for the year [25].

点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Ruizhe Liu等 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 本文第一作者为香港大学InfoBodied AI实验室博士生刘瑞哲，合作者包括周佩、罗谦（同属忆生科技）和孙力。通讯作者为香港大学数据科学研究院及电机电子工 程系助理教授杨言超，以及阿里巴巴达摩院研究员岑俊和宋奕兵。InfoBodied AI实验室在CVPR、ICML、NeurIPS、ICLR等顶会持续发表代表性成果，与国内外知 名高校，科研机构广泛开展合作。 1 机器人为何需要「概念」？ 机器人操作模型常在训练环境表现优异，却在分布外场景失败。例如，能稳定完成"将杯子放入容器"的策略，仅需改变物体颜色、调整位置或增加隔板，就可能彻 底失效。 港大与阿里达摩院联合提出的HiMaCon指出： 泛化失败的根源不在于策略学习不足，而在于缺乏"操作概念"这一认知层。 人类执行任务时，会自然形成"对齐物体"、"抓取目标"、"规 ...

Core Viewpoint - The ATEC 2025 competition represents a significant step towards achieving general embodied intelligence in robotics, challenging teams to develop robots that can operate autonomously in real-world environments rather than controlled settings [28][30][31]. Group 1: Competition Overview - The ATEC 2025 competition took place in a real-world outdoor setting, featuring diverse terrains such as bridges, hills, and stairs, which posed significant challenges for the participating robots [36][38]. - The event aimed to test the robots' abilities to adapt to unpredictable environments, moving beyond the controlled conditions typically seen in robotic competitions [30][31]. - The competition included four main tasks: garbage sorting, autonomous watering, orienteering, and bridge crossing, each designed to assess the robots' multi-modal perception and decision-making capabilities [47][50]. Group 2: Technical Challenges - The competition highlighted three major technical challenges for robotics: environmental perception and cognition, intelligent decision-making and response, and hardware and computational limitations [52][60][69]. - Environmental perception involves not just recognizing objects but understanding their context and condition, which is complicated by real-world factors like contamination and deformation [59]. - Intelligent decision-making requires robots to adapt to dynamic environments, making real-time decisions based on changing conditions, which is currently a significant limitation in robotic capabilities [62][64]. Group 3: Team Performance and Insights - Out of 396 teams, only 13 advanced to the finals, with the winning team, wongtsai, scoring 434 points through effective preparation and innovative strategies [78][79]. - Teams reported that the greatest challenge in outdoor environments was the unpredictability of conditions, which often led to difficult decisions regarding whether to operate autonomously or remotely [80][81]. - The competition fostered a spirit of collaboration and innovation among participants, with many expressing optimism about the future of robotics in real-world applications [93][96].

Core Insights - The article discusses the VLA-Pilot framework, which allows for zero-shot deployment of pre-trained VLA strategies without the need for fine-tuning or additional data collection, addressing the high costs associated with demonstration data and computational resources [2][6]. Group 1: VLA-Pilot Framework - VLA-Pilot is a plug-and-play inference-time policy steering method that significantly enhances the success rate of existing pre-trained VLA strategies across various tasks and robot entities [2][6]. - The framework has been evaluated on six real-world downstream operational tasks, demonstrating robust zero-shot generalization capabilities [2][6]. Group 2: Challenges and Solutions - Pre-trained VLA strategies experience a notable performance drop during downstream deployment, which can be mitigated through fine-tuning, but this approach is limited by high data and resource requirements [2][6]. - The VLA-Pilot framework provides a data-efficient solution that eliminates the need for fine-tuning, making it more applicable in real-world scenarios [6]. Group 3: Live Event Details - A live session is scheduled for December 9, from 19:30 to 20:30, to discuss the VLA-Pilot framework and its implications [6]. - The session will cover the introduction of VLA models, the VLA-Pilot framework, and its implementation details [7].

Company Overview - The company, Self-Variable Robotics, was established in December 2023, focusing on developing embodied intelligent general models to achieve universal robotics [5] - The founder and CEO, Wang Qian, is a graduate of Tsinghua University and one of the earliest scholars to introduce attention mechanisms in neural networks [1] - Co-founder and CTO, Wang Hao, holds a PhD in computational physics from Peking University and has led the development of significant multimodal models in China [3] Technology and Development - Self-Variable Robotics has established a technology path that integrates end-to-end unified models for general embodied intelligence, with a simultaneous development of software and hardware [5] - The company has developed the "WALL-A" model, which is claimed to be the largest end-to-end unified embodied intelligence model globally, surpassing existing models in multiple dimensions [8] - The company emphasizes the importance of real data for training algorithms and maintains a high proportion of PhD-level researchers within its teams [8] Commercial Applications - The company has identified commercial applications in various sectors, including hotels, elderly care, logistics, industry, and hospitals [5] - It is actively recruiting talented individuals in the field of embodied intelligence to drive the implementation of general artificial intelligence [5] Job Opportunities - The company is offering various positions, including algorithm engineers focused on reinforcement learning, world model development, and physical simulation [9][20][24] - Candidates are expected to have strong backgrounds in computer vision, artificial intelligence, robotics, and related fields, with proficiency in deep learning frameworks [13][17][23]

Core Viewpoint - The company aims to establish a community focused on embodied intelligence and robotics, inviting influential figures to collaborate in various areas such as course development, consulting services, and hardware research [1]. Group 1: Course Development and Academic Support - The company seeks to develop courses that benefit beginners and promote industry advancement, targeting both consumer (C-end) and enterprise training, as well as academic curriculum development [2][3]. Group 2: Hardware Development - The company aims to create an affordable and user-friendly research platform for embodied intelligence, ensuring accessibility for developers and ease of use for beginners [4][5]. Group 3: Consulting and Training Services - The company plans to provide consulting services for both B-end and C-end clients in areas such as embodied data, ontology, algorithms, and deployment, supporting industry upgrades and talent development while ensuring personal privacy for employees [6][7]. Group 4: Recruitment and Compensation - The company is looking for individuals with engineering experience in the field or those holding a PhD or higher, offering competitive industry compensation and access to industry resources [8][9].

点击下方 卡片 ，关注" 具身智能 之心 "公众号 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 美的作为国内率先从事VLA相关技术研究的企业，近期正式推出了六臂轮足式人形机器人，形态让人一眼想起了美杜莎hhh。 12月5日，在"2025粤港澳大湾区新经济发展论坛暨21世纪科技年会"上，美的集团副总裁兼首席技术官（CTO）卫昶在主题演讲中首次正式披露超人形机器人MIRO U。 MIRO U作为行业首创的六臂轮足式人形机器人，核心技术体系自主研发构建，可实现稳定升降与360度原地转体，以及机械臂高精度灵活控制，同时执行器支持末 端多类模组的快速切换，形成多维度协同作业系统。 为什么是这种构型？作为一直关注工业制造领域的企业，美的一直想要重点突破工业场景下的作业效率，这也被认为是人形机器人落地的关键。 全平台服务米啦! 保姆级 具身智能方向论文辅导来啦! 我们提供的辅导服务 顶会 / 顶刊 / SCI / EI / 中文核心 毕业论文 / 申博辅导 ...

Core Insights - NVIDIA has officially released CUDA Toolkit 13.1, marking it as the largest update in 20 years [2][4]. Group 1: CUDA Tile - CUDA Tile is the most significant update in NVIDIA CUDA Toolkit 13.1, introducing a tile-based programming model that allows developers to write algorithms at a higher abstraction level [4][5]. - The CUDA Tile model enables developers to specify data blocks called "Tiles" and define mathematical operations on them, allowing the compiler and runtime to optimally distribute workloads across threads [8][15]. - This model abstracts the details of specialized hardware like Tensor Cores, ensuring compatibility with future GPU architectures [9][15]. - CUDA 13.1 includes two components for Tile programming: CUDA Tile IR, a new virtual instruction set architecture, and cuTile Python, a domain-specific language for writing array and Tile-based kernel functions in Python [10]. Group 2: Green Context Support - The update introduces runtime support for Green Contexts, which are lightweight contexts that allow finer-grained GPU resource allocation [20][21]. - Green Contexts enable users to define and manage independent partitions of GPU resources, enhancing the ability to prioritize tasks based on latency sensitivity [21]. Group 3: Multi-Process Service (MPS) Updates - CUDA 13.1 brings several new features to MPS, including Memory Locality Optimization Partition (MLOPart), which allows users to create CUDA devices optimized for memory locality [24][25]. - MLOPart devices are derived from the same physical GPU but present as multiple independent devices with reduced computational resources [25][26]. - Static Streaming Multiprocessor (SM) partitioning is introduced as an alternative to dynamic resource provisioning, providing deterministic resource allocation for MPS clients [29]. Group 4: Developer Tools Enhancements - The release includes performance analysis tools for CUDA Tile kernel functions, enhancing the ability to analyze Tile statistics [33]. - NVIDIA Compute Sanitizer has been updated to support compile-time patching, improving memory error detection capabilities [34]. - New features in NVIDIA Nsight Systems include enhanced tracing capabilities for CUDA applications, allowing for better performance analysis [37]. Group 5: Core CUDA Libraries Updates - CUDA 13.1 introduces performance updates for cuBLAS on the Blackwell architecture, including support for block-scaled FP4 and FP8 matrix multiplication [40]. - The cuSOLVER library has been optimized for batch processing of eigenvalue problems, achieving significant performance improvements [42].

点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Pei Yang等 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 在 embodied AI 领域，视觉 - 语言 - 动作（VLA）模型与世界模型的发展虽展现出通用自主能力的巨大潜力，却始终受限于核心瓶颈——大规模、多样化机器人训 练数据的稀缺。现有解决方案要么依赖成本高昂的真实机器人数据采集，要么通过简单叠加机器人部件编辑第一视角人类视频，均无法应对第三人称视频中的全身 复杂动作、动态背景与严重遮挡问题。 新加坡国立大学 Show Lab 提出的 X-Humanoid 框架 ，以 "数据合成 - 模型适配 - 大规模生成" 为核心逻辑，首次实现第三人称人类视频到类人机器人视频的高质量 转化，为机器人训练提供了全新的数据生成范式。 论文题目：X-Humanoid: Robotize Human Videos to Generate Humanoid Videos at Scale 项 ...