正文： 梅开四度， 国内领先通用具身智能企业[极佳视界]完成亿元级A1轮融资！ 近日，Physical AI（物理AI）领域头部创企 [极佳视界 ]宣布完成 新一轮亿元级A1轮融资，本轮融资由华为哈 勃、华控基金联合投资 。 值的注意的是，公司于8月28日刚完成Pre-A、Pre-A+两轮数亿元融资，其中 Pre-A轮融资由国中资本领投，紫峰 资本、老股东 PKSHA Algorithm Fund跟投；Pre-A+轮融资由中金资本、广州产投、一村淞灵、华强资本投资； 以及于今年2月份完成由 普超资本、合鼎共资本、上海天使会投资联合投资的 数千万天使++轮融资。 温馨提示 ： 点击下方图片，查看运营团队最新原创报告（共235页） 说明： 欢迎约稿、刊例合作、行业交流 ， 行业交流记得先加入 "机器人头条"知识星球 ，后添加（ 微信号：lietou100w ）微 信； 若有侵权、改稿请联系编辑运营（微信：li_sir_2020）； 有关科技大厂入局具身赛道（大模型赋能、投资和自研）更多详细梳理、解读，已放到知识星 球"机器人头条"（点击后方链接，加入星球查看） ： 【 原创】多家顶尖科技大厂，进军人形机器人整机制 ...

Core Insights - Embodied intelligence has gained significant traction globally, with Figure achieving a valuation of $39 billion despite zero revenue, while domestic players are securing commercial orders and projecting substantial revenue growth [1][2][9] - The Chinese government has integrated embodied intelligence into its strategic planning, emphasizing its importance in the industrial landscape [1][9] - The market for embodied intelligence is projected to reach trillions, with both China and the U.S. competing vigorously in this emerging sector [1][6] Definition and Understanding - Embodied intelligence is recognized as a crucial development in artificial intelligence, characterized by agents that interact with their environment through a physical body, showcasing autonomy and adaptability [2] - It represents a convergence of machine learning, computer vision, and robotics, marking a significant step towards practical AI applications [2] Commercial Applications - Different forms of embodied intelligence robots are evolving to meet diverse needs across sectors such as retail, dining, logistics, and healthcare [4] - Commercial applications focus on enhancing service experiences and operational flexibility in dynamic environments, while industrial applications prioritize efficiency and safety in structured settings [4] Strategic Importance - Embodied intelligence is pivotal in narrowing the technological gap between China and the U.S., driving innovation across various industrial sectors [6] - The competition in embodied intelligence is not only about economic benefits but also about enhancing national competitiveness and technological self-reliance [6] Policy Support - The Chinese government has actively promoted the development of embodied intelligence through various policies, funding initiatives, and standardization efforts [9] - Local governments are also implementing plans and pilot projects to support the industry, establishing funds and alliances to foster collaboration [9] Development Stages - The evolution of embodied intelligence can be categorized into three phases: conceptual development, technological accumulation, and application expansion driven by large models [11] - The current phase is characterized by rapid advancements, with the U.S. leveraging its computational advantages while China accelerates its catch-up through policy support and industrial collaboration [11] Bottlenecks and Challenges - The industry faces significant challenges, including data scarcity, technological maturity, and high costs associated with core components and computational resources [13][16] - The lack of high-quality operational data and the need for advancements in dexterous manipulation and generalization capabilities are critical hurdles [13] Data Acquisition and Solutions - Current data acquisition methods include remote operation, simulation, motion capture, and internet video, but high-quality data remains scarce [16] - The industry is exploring solutions such as "world models" and data collection training grounds to alleviate data challenges, with cities like Beijing and Shanghai accelerating the establishment of these facilities [19] Model Evolution - The VLA model is emerging as a consensus for development, integrating large language model reasoning with real-world perception and action capabilities [21] - This evolution is expected to lead to a significant leap in embodied intelligence capabilities, akin to the breakthroughs seen with large language models [21] Commercialization Trends - The commercialization of embodied intelligence is progressing through various stages, with initial applications focusing on low-complexity, high-ROI scenarios [31] - The industry is transitioning from hardware sales to service subscription models, indicating a shift towards more integrated business approaches [35] Global Market Outlook - The global market for embodied intelligence is anticipated to experience exponential growth, with projections indicating a market size of approximately 192 billion RMB by 2025 and a compound annual growth rate of 73% over the next five years [46] - China's market is expected to see significant growth, potentially reaching over 280 billion RMB by 2035, driven by a robust industrial ecosystem and competitive supply chains [50] Competitive Landscape - The competition in the embodied intelligence sector is characterized by three main players: AI-native challengers, traditional industrial players, and cross-industry giants [55] - The market is witnessing a trend towards consolidation, with product homogenization emerging as a concern, suggesting an impending wave of industry consolidation [57] Initial Players and Innovations - Companies like Tesla and Figure AI are leading the charge in developing humanoid robots, with Figure AI's valuation reaching $39 billion [64] - Innovations in dexterous manipulation and core component integration are critical for advancing the capabilities of humanoid robots [83][88]

今年迄今，优必选（09880.HK）已交付约200台人形机器人，2025年全年交付量约500台。公司管理层 预估，人形机器人业务今年将为公司贡献约4亿元营收。2026年，优必选计划交付2000至3000台人形机 器人。国际投行摩根士丹利在11月17日一份调研报告中披露上述信息。 与此同时，优必选首席品牌官谭旻于11月18日对外表示，公司已制定产能爬坡计划，预计到2026年，工 业人形机器人年产能将达5000台，2027年进一步扩大至10000台规模。 此前8月中旬，优必选副总裁、研究院院长焦继超曾向南都记者介绍，优必选预计今年交付500台工业版 Walker系列人形机器人，面向汽车、3C半导体等行业客户。相比之下，2024年仅出货10台人形机器 人。 今年下半年以来，优必选接连宣布多笔大额订单。截至11月10日，Walker系列人形机器人全年累计订单 金额已突破8亿元。背后的买家以汽车产业链公司为主，如东风柳汽、天奇股份、觅亿汽车等。 11月17日，优必选在海外社交平台介绍，已开始批量交付首批数百台Walker S2机器人，覆盖汽车制 造、智能工厂、智能物流和数据采集中心等领域。 为展示交付能力，优必选于11 ...

上个月，小鹏汽车的市值短暂超过了理想汽车，成为国内市值最高的造车新势力公司。然而「汽车」这 个词，是两家公司都想快速甩掉的。 作者 |新皮层NewNewThing 吴一凡 本文字数：4836，阅读时长大约8分钟 封图 |何小鹏 小鹏想要甩掉它的速度比理想还要快。 2025.11.13 11月5日，小鹏汽车在广州举办科技日，slogan从去年的「未来出行探索者，面向全球的AI汽车公 司」，升级为「物理AI世界的出行探索者，面向全球的具身智能公司」——仅仅是汽车的AI化已不足 以满足这家公司对自身战略和市值的渴望。科技日中，小鹏汽车发布了第二代VLA模型，以及包括 Robotaxi、第二代IRON人形机器人和飞行汽车在内的三大具身智能产品。 何小鹏认为，继互联网、移动互联网、数字AI时代后，数字世界和物理世界的融合将催生「物理 AI」，机器将逐步拥有理解、交互和改变世界的能力。在这个概念下，小鹏汽车的业务涵盖物理AI时 代的技术底座，从模型到芯片，从基础设施到上层终端，比如汽车、Robotaxi、人形机器人和飞行汽 车。 证明了其二代IRON人形机器人里没有真人之后，小鹏美股股价在11月6日盘中一度涨超14%，市 ...

Core Insights - The "14th Five-Year Plan" identifies embodied intelligence as a core growth point for future industries, marking a significant transition in the automotive sector from "transportation tools" to "intelligent mobility entities" [1][2] - The synergy between policy support and technological advancements is reshaping the automotive industry, driving a shift from traditional manufacturing to a comprehensive "intelligent manufacturing + services" model [1][2] Policy and Hardware Empowerment - The strategic positioning of embodied intelligence in the "14th Five-Year Plan" is part of a systematic approach to strengthen manufacturing and digitalization in China, with the automotive industry as a key application area [2] - The Ministry of Industry and Information Technology is focusing on critical technologies such as automotive AI and operating systems, while pilot cities for "vehicle-road-cloud integration" are being rapidly developed to support the application of embodied intelligent vehicles [2] Technological Synergy - The Xiaopeng Iron Robot utilizes AI chips and systems that leverage the company's long-term investments in intelligent driving, showcasing a shared technological foundation between automotive and robotics sectors [3][5] - The collaboration between automotive companies and robotics is becoming a trend, with companies like Changan and Huawei extending their technological capabilities across both domains [5][6] Industry Transformation - The emergence of embodied intelligence is shifting the competitive landscape of the automotive industry from hardware manufacturing to intelligent capabilities, prompting traditional automakers to evolve into "intelligent operators" [6][9] - The integration of robotics into automotive manufacturing processes is demonstrating the feasibility of transferring automotive technologies to robotics, thereby creating natural technological barriers for automotive companies [6][8] Market Expansion and Future Potential - The integration of embodied intelligence is leading to diverse application scenarios, expanding the automotive industry's boundaries from manufacturing to service sectors [10][12] - The market potential for embodied intelligence in the automotive sector is projected to grow significantly, with estimates suggesting a shift from a billion-dollar to a trillion-dollar market, driven by advancements in technology and supportive policies [12][13]

Core Viewpoint - The article discusses the recent launch of the IRON robot by Xiaopeng Motors, highlighting its humanoid design and the significant media attention it has garnered, while also addressing the skepticism surrounding its capabilities and production readiness [3][5][11]. Group 1: Xiaopeng's Robot Launch - Xiaopeng Motors unveiled the IRON robot, which resembles a humanoid figure, generating excitement comparable to major tech events like those of Elon Musk [3][5]. - The launch event led to a surge in Xiaopeng's stock price, increasing by 14%, indicating a positive market reaction and renewed interest from institutional investors [5][9]. - The event was strategically designed to capture public interest, with social media discussions reaching over 200 million views [5][9]. Group 2: Technical Aspects of the IRON Robot - The IRON robot features a fully humanoid structure, including a skeletal system that mimics human spine curvature, allowing for natural movements [14]. - It incorporates innovative materials, such as lattice structures for muscle layers, providing both rigidity and flexibility, and a skin-like covering with tactile sensors for emotional interaction [14][21]. - Xiaopeng's approach to robotics emphasizes the need for humanoid designs to fit into human-centric environments, marking a significant shift from previous four-legged designs [11][14]. Group 3: Industry Context and Competition - The automotive industry is increasingly venturing into humanoid robotics, with companies like Xiaomi and FAW Group also developing their own humanoid robots [16][18]. - Xiaopeng Motors leverages its existing automotive technology and expertise to reduce research and development costs in the robotics sector, as both fields share significant technological overlaps [18][19]. - Despite the advancements, Xiaopeng's automotive business is still facing challenges, including a reported net loss of 1.14 billion yuan in the first half of the year [22][25].

Core Insights - The year 2024 is anticipated to be a pivotal year for humanoid robots, with expectations for more applications in various sectors, particularly in industrial and commercial settings [1][2][4] - The humanoid robot industry is evolving from basic manufacturing to more specialized and complex applications, aiming to establish a complete humanoid robot industry chain [1][6] Group 1: Industry Trends - Humanoid robots are currently utilized in performance, interaction, and exhibition guide roles, but face challenges in large-scale delivery in industrial settings [1][2] - The integration of embodied intelligence with industrial robots is seen as crucial for addressing challenges in flexible manufacturing and efficiency [2][6] - The industry is moving towards more refined and technically intensive applications, with a focus on enhancing the flexibility and capabilities of robots [6][9] Group 2: Market Opportunities - There is a significant opportunity for Chinese robot companies to expand internationally, leveraging their manufacturing and scenario advantages [6][4] - The development of autonomous logistics vehicles is expected to address last-mile delivery challenges, although they face hurdles in accurately processing a large number of SKUs [4][6] - Small humanoid robots are gaining traction in entertainment and education, with potential factory applications within five years [4][6] Group 3: Technological Challenges - The large-scale delivery of humanoid robots is hindered by the need for a complete closed-loop control system that includes perception, decision-making, and execution [6][9] - Current challenges include the need for improved performance parameters and mass production capabilities in emerging fields like tactile sensors [6][9] - The transition from traditional automation to intelligent partners requires significant advancements in software algorithms and integration of ecosystem resources [9][10]

Core Insights - Tesla has resumed sharing updates on its autonomous driving algorithms after a two-year hiatus, presenting at the ICCV conference instead of its previous AI Day events [1] - The company is facing challenges with its end-to-end architecture for autonomous driving, particularly regarding the "black box" nature of the model and the quality of training data [3][7] Group 1: Technical Developments - Tesla's end-to-end system must address the mapping from high-dimensional to low-dimensional outputs, which is complex due to the nature of the data [5][7] - The company has implemented optimizations in its architecture, including the introduction of OCC occupancy networks and 3D Gaussian features to enhance decision-making [3][8] - Tesla has developed a "neural world simulator" that serves as both a training and validation environment for its algorithms, allowing for extensive testing and refinement [12][15] Group 2: Competitive Landscape - Other companies in the industry, such as Xpeng and Li Auto, have also adopted similar models, indicating a shift in the competitive dynamics of the autonomous driving sector [4][11] - Tesla's previous position as a leader in autonomous driving technology is being challenged, with other players no longer closely following its developments [18] Group 3: Market Reception and Challenges - The subscription rate for Tesla's Full Self-Driving (FSD) feature is low, with only about 12% of users opting for it, raising concerns about the technology's acceptance [4][24] - Despite price adjustments for FSD, consumer interest has waned, with many potential buyers citing concerns over the technology's maturity and reliability [24][25] - Recent investigations into Tesla's FSD have highlighted safety issues, further complicating the company's efforts to promote its autonomous driving capabilities [24][25]

Core Viewpoint - The article emphasizes the significant advancements in robotics, particularly in robot learning, driven by the development of large models and multi-modal AI technologies, which have transformed traditional robotics into a more learning-based paradigm [3][4]. Group 1: Introduction to Robot Learning - The article introduces a comprehensive tutorial on modern robot learning, covering foundational principles of reinforcement learning and imitation learning, leading to the development of general-purpose, language-conditioned models [4][12]. - HuggingFace and Oxford University researchers have created a valuable resource for newcomers to the field, providing an accessible guide to robot learning [3][4]. Group 2: Classic Robotics - Classic robotics relies on explicit modeling through kinematics and control planning, while learning-based methods utilize deep reinforcement learning and expert demonstration for implicit modeling [15]. - Traditional robotic systems follow a modular pipeline, including perception, state estimation, planning, and control [16]. Group 3: Learning-Based Robotics - Learning-based robotics integrates perception and control more closely, adapts to tasks and entities, and reduces the need for expert modeling [26]. - The tutorial highlights the challenges of safety and efficiency in real-world applications, particularly during the initial training phases, and discusses advanced techniques like simulation training and domain randomization to mitigate risks [34][35]. Group 4: Reinforcement Learning - Reinforcement learning allows robots to autonomously learn optimal behavior strategies through trial and error, showcasing significant potential in various scenarios [28]. - The tutorial discusses the complexity of integrating multiple system components and the limitations of traditional physics-based models, which often oversimplify real-world phenomena [30]. Group 5: Imitation Learning - Imitation learning offers a more direct learning path for robots by replicating expert actions through behavior cloning, avoiding complex reward function designs [41]. - The tutorial addresses challenges such as compound errors and handling multi-modal behaviors in expert demonstrations [41][42]. Group 6: Advanced Techniques in Imitation Learning - The article introduces advanced imitation learning methods based on generative models, such as Action Chunking with Transformers (ACT) and Diffusion Policy, which effectively model multi-modal data [43][45]. - Diffusion Policy demonstrates strong performance in various tasks with minimal demonstration data, requiring only 50-150 demonstrations for training [45]. Group 7: General Robot Policies - The tutorial envisions the development of general robot policies capable of operating across tasks and devices, inspired by large-scale open robot datasets and powerful visual-language models [52][53]. - Two cutting-edge visual-language-action (VLA) models, π₀ and SmolVLA, are highlighted for their ability to understand visual and language instructions and generate precise control commands [53][56]. Group 8: Model Efficiency - SmolVLA represents a trend towards model miniaturization and open-sourcing, achieving high performance with significantly reduced parameter counts and memory consumption compared to π₀ [56][58].

Core Viewpoint - The article emphasizes the significant advancements in the field of robotics, particularly in robot learning, driven by the development of artificial intelligence technologies such as large models and multi-modal models. This shift has transformed traditional robotics into a learning-based paradigm, opening new potentials for autonomous decision-making robots [2]. Group 1: Introduction to Robot Learning - The article highlights the evolution of robotics from explicit modeling to implicit modeling, marking a fundamental change in motion generation methods. Traditional robotics relied on explicit modeling, while learning-based methods utilize deep reinforcement learning and expert demonstration learning for implicit modeling [15]. - A comprehensive tutorial provided by HuggingFace and researchers from Oxford University serves as a valuable resource for newcomers to modern robot learning, covering foundational principles of reinforcement learning and imitation learning [3][4]. Group 2: Learning-Based Robotics - Learning-based robotics simplifies the process from perception to action by training a unified high-level controller that can directly handle high-dimensional, unstructured perception-motion information without relying on a dynamics model [33]. - The tutorial addresses challenges in real-world applications, such as safety and efficiency issues during initial training phases, and high trial-and-error costs in physical environments. It introduces advanced techniques like simulator training and domain randomization to mitigate these risks [34][35]. Group 3: Reinforcement Learning - Reinforcement learning allows robots to autonomously learn optimal behavior strategies through trial and error, showcasing significant potential across various scenarios [28]. - The tutorial discusses the "Offline-to-Online" reinforcement learning framework, which enhances sample efficiency and safety by utilizing pre-collected expert data. The HIL-SERL method exemplifies this approach, enabling robots to master complex real-world tasks with near 100% success rates in just 1-2 hours of training [36][39]. Group 4: Imitation Learning - Imitation learning offers a more direct learning path for robots by replicating expert actions through behavior cloning, avoiding complex reward function designs and ensuring training safety [41]. - The tutorial presents advanced imitation learning methods based on generative models, such as Action Chunking with Transformers (ACT) and Diffusion Policy, which effectively model multi-modal data by learning the latent distribution of expert behaviors [42][43]. Group 5: Universal Robot Policies - The article envisions the future of robotics in developing universal robot policies capable of operating across tasks and devices, inspired by the emergence of large-scale open robot datasets and powerful visual-language models (VLMs) [52]. - Two cutting-edge VLA models, π₀ and SmolVLA, are highlighted for their ability to understand visual and language instructions and generate precise robot control commands, with SmolVLA being a compact, open-source model that significantly reduces application barriers [53][56].