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 ...

Core Viewpoint - The article discusses the challenges and advancements in the VLA (Variable Learning Algorithm) field, emphasizing the importance of real machine data and practical experience in achieving effective results in embodied intelligence applications. Group 1: Data Collection - Data collection methods for VLA primarily include imitation learning and reinforcement learning, with remote operation, VR, and full-body motion capture being key techniques [8][9] - The quality of data collected is crucial, and methods like real2sim2real are highlighted as important for effective data acquisition [8] Group 2: VLA Training - Before deploying models in real machines, simulation debugging is essential, especially when real machine data is insufficient [10] - Training techniques are critical, with challenges in fine-tuning models and achieving good results with small data sets [10] - Some algorithms, like ACT, are easier to train, while others, such as π0 and π0.5, require more intricate techniques and experience [10] Group 3: VLA Deployment - After training, models often need to be "slimmed down" due to their large parameter sizes, which poses challenges for deployment on edge chips [12] - Techniques like quantization and distillation are necessary to minimize parameter size while maintaining performance [12] Group 4: Educational Initiatives - The article introduces a practical course aimed at helping individuals learn VLA effectively, covering various aspects such as hardware, data collection, algorithms, and deployment [13][16] - The course is designed for a wide audience, including students and professionals looking to transition into the embodied intelligence field [27]

Core Viewpoint - SpaceX is poised to become the highest-valued private company globally, with a potential valuation of $800 billion, surpassing OpenAI's $500 billion valuation [1][2][12]. Valuation Insights - SpaceX is negotiating a new round of internal stock sales that could elevate its valuation to $800 billion, nearly equivalent to Switzerland's GDP of $900 billion [1]. - The company's valuation has doubled from $400 billion in July 2023 to $800 billion in less than six months [3][10]. - There is speculation that the final valuation could be around $560 billion if shares are priced at approximately $300 each [11]. Business Overview - Founded in 2002, SpaceX is a private aerospace and space transportation company based in Texas, aiming to reduce space launch costs and facilitate human colonization of Mars [5][6]. - SpaceX has two core business segments: rocket launches and satellite services, with a dominant position in the rocket launch market [17][18]. Satellite Business - The satellite business is a significant driver of SpaceX's high valuation, with the Starlink division operating around 9,000 satellites, contributing to revenue [19][23]. - SpaceX's revenue for 2023 is projected to reach $15.5 billion, slightly exceeding OpenAI's expected revenue of $13 billion [25][26]. IPO Expectations - SpaceX is expected to go public in the second half of next year, potentially ahead of OpenAI, which is rumored to file for an IPO in 2026 [16][26]. - There is a possibility that SpaceX may bundle its rocket and satellite businesses for the IPO, rather than spinning off Starlink as a separate entity [24].

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。 点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Yunfei Li等 在机器人操作领域，视觉 - 语言 - 动作（VLA）模型虽已实现多任务泛化，却始终难以突破长时程精细操作的核心瓶颈——以系鞋带为例，该任务需同时满足 "毫 米级穿洞精度""柔性鞋带操控""多步误差规避" 三大要求，传统方法或通用 VLA 模型均因能力短板无法胜任。 字节跳动提出的 GR-RL 框架 ，以 "问题定位 - 方案设计 - 落地验证" 为逻辑主线，通过多阶段训练 pipeline 将通用 VLA 模型转化为长时程精细操作专家，成为首个 能自主完成多鞋孔系鞋带的学习型模型。 论文题目：GR-RL: Going Dexterous and Precise for Long-Horizon Robotic Manipulation 项目链接：https://seed.bytedance.com/gr_rl 作者单位 ...

Group 1 - The company is preparing a comprehensive research report on the embodied industry, expected to be released in the first quarter of next year [1] - The report will cover various aspects including financing, industry trends, policies, algorithms, implementation, and exports related to embodied companies [1] - The company is conducting a survey to understand which topics are of most interest to stakeholders, allowing for multiple selections [2] Group 2 - Key areas of focus for the survey include domestic and international embodied industry conditions, financing and business status of embodied companies, data collection related to embodiment, algorithm optimization and deployment, edge chips for robotics, downstream industry development, talent structure and demand in the embodied industry, and guidance for company listings [4]