银河通用宣布完成超3亿美元新一轮融资，这一金额刷新了具身智能领域的单轮融资纪录。 据19日公布的信息，本轮融资由中国移动链长基金领投，中金资本、中科院基金、苏创投、央视融媒体 基金、天奇股份等产业资本与投资平台联合注资。此外，交易还吸引了来自新加坡、中东的国际投资机 构及老股东的加注。融资完成后，银河通用的估值已达到30亿美元（约211.3亿人民币）。 这笔交易凸显了资本市场对具身智能赛道的持续押注。作为北京大学系的初创企业，银河通用在短短半 年内估值翻了三倍。此前一轮融资后，其外部估值约为10亿美元。随着新资金的注入，其累计融资额已 约达8亿美元。公司表示，资金将主要用于核心技术的持续投入、加速各领域解决方案的规模化落地与 迭代，以及拓展全球合作网络。 此次融资正值中国人形机器人企业加速迈向资本市场的关键窗口期。据报道，银河通用已于11月28日完 成股份制改革，目前正在筹备赴港上市，最早可能在明年首季向港交所递交申请，目标估值在30至40亿 美元之间。 "国家队"与产业资本重注 在完成本轮融资前，银河通用已在资本市场动作频频。公司于今年6月23日完成了由宁德时代领投的11 亿元新一轮融资，并在今年6月和11 ...

人形机器人，迎来一针暴力强心剂。 与宁德时代和溥泉资本领投的上一轮，也就是6月份那轮融资相比，投资方阵容变化不小，更多元化， 传了许久的中东资本也终于落地，能看出来银河通用在试图构建自己国际化的股东生态，而且继宁德时 代后又来了个央企"大家伙"——中国移动。 截至目前，银河通用累计获得的融资金额已经约8亿美元（合56亿人民币），而半年前这个数字还只有 24亿元。由于某种未知原因，银河通用还另有大额融资未公布。 投中网获悉，银河通用已完成新一轮融资，规模超过3亿美元（约合超21亿元人民币），投后估值超过 30亿美元（超200亿元人民币）。这两个数字意味着，国内人形机器人赛道的单笔最大融资额，以及估 值天花板，双双被刷新了。 再来看投资方，本轮融资由中国移动链长基金领投，中金资本、中科院基金、苏创投、央视融媒体基 金、天奇股份等投资平台及产业巨头，同时也获得新加坡及中东国际投资机构的注资及老股东追加投 资。 另外，投中网还独家获悉，银河通用已经与某单一产业方签订了一笔G1机器人采购合同，规模达到 1000台，如果按照G1约70万元的售价计算，合同金额将达到7亿元。 7亿元的订单，是什么概念？要知道宇树和智元这两家 ...

Group 1: Generative AI Developments - OpenRouter platform has launched the anonymous model Polaris Alpha, believed to be a variant of GPT-5.1, with a knowledge base cutoff in October 2024 and a maximum context capacity of 256K and a single output limit of 128K [1] - Polaris Alpha shows smooth performance in desk work and programming tasks, exhibiting typical GPT characteristics and supporting NSFW mode [1] - The model is currently available for free via API, demonstrating good performance in programming mini-games and web design, with GPT-5.1 expected to be officially released in mid-November [1] Group 2: Multi-Modal Intelligence - A new multi-modal paradigm called Cambrian-S has been proposed by researchers including Yann LeCun, focusing on "spatial super-perception" and marking the first step in exploring video spatial super-perception [2] - The research outlines a development path for multi-modal intelligence across four levels: semantic perception, streaming event cognition, 3D spatial cognition, and predictive world modeling, introducing the VSI-SUPER benchmark for spatial super-perception capabilities [2] - Cambrian-S utilizes latent variable frame prediction to manage memory and event segmentation through a "surprise" signal, outperforming Gemini in spatial cognition tasks with smaller models [2] Group 3: AI Programming Tools - Meituan has launched an AI IDE programming tool named CatPaw, featuring code completion, agent Q&A generation, built-in browser preview debugging, and project-level analysis [3] - The core engine of CatPaw is Meituan's self-developed LongCat model, fully compatible with major programming languages like Python, C++, and Java, and currently available for free [3] - Over 80% of weekly active users among Meituan's internal developers utilize CatPaw, with AI-generated code accounting for about 50% of new code submissions, and a Windows version expected to launch soon [3] Group 4: Domestic AI IDE Launch - YunSi Intelligence has introduced Vinsoo, the world's first AI IDE equipped with a cloud-based security agent, surpassing products like Cursor and Codex that utilize Claude [4] - Vinsoo achieves breakthroughs in long-context engineering algorithms, supporting effective context lengths in the millions and allowing up to eight intelligent agents to operate simultaneously [4] - The new Beta 3.0 version supports cloud-based one-click publishing, mobile usage, and team collaboration, led by a founding team of post-00s graduates from top universities in China and the U.S. [4] Group 5: Open Source Audio Editing Model - Jieyue Xingchen has released the first open-source LLM-level audio editing model, Step-Audio-EditX, which allows precise control over audio emotions, speaking styles, and paralinguistic features through language commands [5] - The model employs a unified LLM framework and a "dual-codebook" audio tokenizer structure, supporting zero-shot text-to-speech, iterative editing, and bilingual capabilities [5] - With approximately 3 billion parameters, the model can run on a single 32GB GPU, achieving higher accuracy in emotion and style control compared to closed-source models like MiniMax and Doubao [5] Group 6: AI Glasses Launch - Baidu has officially launched the Xiaodu AI glasses Pro, priced at 2299 yuan, with a promotional price of 2199 yuan for Double Eleven, weighing 39 grams and featuring a 12-megapixel wide-angle camera [6] - The glasses integrate multi-modal AI models, offering functionalities such as photography, music recognition, AI translation, object recognition, note-taking, and audio recording, with real-time translation capabilities [6] - Similar to Xiaomi's AI glasses, these are not the more advanced AI+AR glasses currently available [6] Group 7: Robotics Innovation - Galaxy General has introduced the DexNDM, a dexterous hand neural dynamics model that achieves stable, multi-axial rotation operations on various objects, capable of using tools like screwdrivers and hammers [8] - The DexNDM model disassembles hand-object interactions to the joint level, utilizing a training process that allows for stable operations across tasks and forms without requiring successful examples [8] - This technology has been applied to remote operation systems, enabling operators to give high-level commands via VR controllers while DexNDM autonomously manages fine control at the finger level [8] Group 8: Insights on AI Entrepreneurship - A YC partner emphasizes that AI tools cannot replace a founder's sales capabilities, suggesting that AI should first target quick-to-implement entry points in traditional industries rather than aiming for full automation [9] - The core competitive advantage in early-stage entrepreneurship is "learning speed" rather than scale, with a focus on quickly validating ideas with small customers [9] - AI sales development representatives (SDRs) are effective only when there are already well-functioning sales processes, and founders must clarify their target audience and attention acquisition strategies for AI tools to be effective [9]

Core Insights - The article discusses the breakthrough of the DexNDM model developed by Galaxy Universal, which enables dexterous hands to perform complex tasks such as in-hand rotation and tool usage, bridging the gap between simulation and real-world applications [2][4][55]. Group 1: DexNDM Model Capabilities - DexNDM allows for stable in-hand rotation of various objects, regardless of their size or shape, achieving cross-object and cross-pose manipulation [5][6]. - The model can operate under challenging wrist postures, enabling continuous rotation of long objects and stable manipulation of small items [6][17]. - It enhances the robot's ability to perform complex tasks like screw tightening and furniture assembly, marking a significant leap from simple grasping to dexterous manipulation [21][64]. Group 2: Technical Innovations - DexNDM employs a joint-wise neural dynamics model, allowing each joint to independently predict its next state, improving data efficiency and generalization across different tasks [8][10]. - The model utilizes an automated data collection strategy to generate rich contact data without manual intervention, enhancing learning efficiency [11][14]. - A residual policy network is trained to bridge the gap between simulation and reality, facilitating the transfer of learned strategies to real-world scenarios [15]. Group 3: Importance of Dexterous Manipulation - Dexterous manipulation is crucial for robots to transition from basic capabilities to productive tasks, as it encompasses both motion and operational abilities [24][28]. - The ability to perform in-hand rotation and tool usage is seen as a pinnacle of dexterous manipulation, representing a significant challenge in robotics research [37][38]. - The advancements in dexterous manipulation are expected to lead to robots that can perform a wide range of tasks, moving beyond simple demonstrations to actual productive capabilities [58][65].

Core Insights - The article discusses the development of DexNDM, a new method aimed at solving the sim-to-real challenge in dexterous robotic manipulation, particularly in achieving stable in-hand rotation of various objects [2][5][31] Group 1: Background and Challenges - High dexterity in remote operation of complex tools, such as using a screwdriver or hammer, has been a long-standing challenge in robotics [4] - Traditional direct mapping remote operation methods are limited to simple tasks and cannot handle complex manipulations requiring fine motor skills [4] Group 2: DexNDM Methodology - DexNDM proposes a semi-autonomous remote operation paradigm that breaks down complex tasks into stable, reliable atomic skills that robots can execute autonomously [5] - The method focuses on learning general, stable atomic skills for in-hand object rotation, covering a wide range of scenarios including challenging elongated and small objects [5][14] Group 3: Key Features and Achievements - DexNDM achieves unprecedented dexterity by enabling continuous rotation of elongated objects and intricate manipulation of small objects under challenging wrist postures [7][14] - The method demonstrates superior performance in manipulating complex geometries compared to previous works, even with more general hardware [14] - It showcases high adaptability to various wrist postures and rotation axes, allowing for precise control regardless of the mechanical hand's orientation [17] Group 4: Robustness and Practical Applications - The DexNDM system exhibits high dexterity and robustness, successfully performing complex tool usage tasks such as tightening screws and assembling furniture [21] - The system's robustness allows it to handle long-horizon assembly tasks without interruption, even in the presence of unforeseen scenarios [21] Group 5: Innovations in Data Collection and Modeling - DexNDM employs a joint-wise neural dynamics model that effectively fits real-world data to bridge the gap between simulation and reality [24] - An automated data collection strategy, termed "chaos box," is utilized to gather diverse interaction data with minimal human intervention [28] - The training of a residual policy network is implemented to compensate for the dynamics gap between simulation and real-world applications [30]

Core Insights - The article discusses the development of DexNDM, a method aimed at solving the sim-to-real challenge in dexterous robotic manipulation, particularly in achieving stable in-hand rotation of various objects [2][5][24]. Group 1: Background and Challenges - High dexterity in remote operation of complex tools, such as using a screwdriver or hammer, has been a long-standing challenge in robotics [4]. - Traditional direct mapping remote operation methods are limited to simple tasks and cannot handle complex manipulations requiring fine motor skills [4]. - A semi-autonomous remote operation paradigm is proposed, which breaks down complex tasks into stable atomic skills that robots can execute autonomously [4]. Group 2: DexNDM Methodology - DexNDM is designed to learn general and stable atomic skills for in-hand rotation, covering a wide range of scenarios including challenging elongated and small objects [5][19]. - The method utilizes a joint-wise neural dynamics model to bridge the gap between simulation and real-world dynamics, enhancing data efficiency and generalization across different hand-object interactions [19][20]. Group 3: Achievements and Capabilities - DexNDM achieves unprecedented capabilities in continuous rotation of objects under challenging wrist postures, demonstrating superior performance compared to previous methods [9][13]. - The system allows operators to guide dexterous hands in complex tasks such as tightening screws and assembling furniture, showcasing its robustness and adaptability [7][15]. - The method's flexibility enables stable execution of tasks regardless of the wrist orientation or rotation axis required [14][15]. Group 4: Data Collection and Training - An automated data collection system, termed "Chaos Box," is developed to gather diverse real-world interaction data with minimal human intervention [21]. - A residual policy network is trained to compensate for the dynamics gap between simulation and reality, enhancing the system's performance in real-world applications [23]. Group 5: Conclusion and Future Outlook - DexNDM represents a significant advancement in addressing the sim-to-real challenge in robotics, achieving dexterous manipulation skills previously deemed impossible [24]. - The authors believe this is just the beginning, with the potential for dexterous hands to play a crucial role in the future of humanoid robotics [25].