允中 发自 凹非寺 量子位 | 公众号 QbitAI 光轮智能 × NVIDIA 重磅直播即将开启！ 双方将携手揭秘如何利用SimReady与AI打通Sim2Real （仿真到现实） 。 直播核心看点 Sim2Real技术突破 深度解析双方如何基于SimReady与AI，实现从虚拟仿真到物理世界的无缝迁移，攻克机器人开发落地中的关键挑战。 合作进展独家披露 光轮智能与NVIDIA在技术研发、场景应用等方面的最新合作成果与规划。 大咖实战视角 两位专家将结合实践经验，分享机器人、AI领域的技术趋势与商业化路径。 主讲嘉宾 直播时间 对机器人及AI领域感兴趣的朋友，欢迎扫码预约，锁定直播席位！ *本文系量子位获授权刊载，观点仅为原作者所有。 一键三连 「点赞」「转发」「小心心」 欢迎在评论区留下你的想法！ 更多详情，请戳文末「阅读原文」。 Steve Xie，光轮智能创始人兼CEO Madison Huang，NVIDIA产品营销高级总监 北京时间 ： 10月9日 凌晨0:00 太平洋时间 ： 10月8日 上午9:00 点亮星标 — 完 — 科技前沿进展每日见 ...

以下文章来源于具身智能之心 ，作者具身智能之心 具身智能之心 . 与世界交互，更进一步 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 然而Physical Intelligence (PI)联合创始人、具身智能领域的先行者Sergey Levine始终坚称：替代数据是叉勺（叉子勺子二合一的产物，既不 如勺子，也不如叉子），真实交互数据不可替代——这究竟是策略局限，还是数据本质的铁律？如今，Genie3携世界模型横空出世，能够 从文本生成可交互的动态环境，甚至驱动在线规划。这是否意味着我们正站在"仿真"与"现实"二元对立终结的前夜？世界模型会成为数据 问题的终极答案，还是仅仅换了一种形式的sim，并依然难逃Sim-to-Real gap的宿命？ 本场技术圆桌，我们邀请到国内Sim2Real领域四位杰出青年科学家—— 与他们四位共话前沿，从高保真3D资产构建、神经渲染的物理瓶颈、铰链体结构优化，到VLA模型的解耦设计等方面入手深入探讨：具身 智能的数据之路，究竟通向仿真、现实，还是那个正在觉醒的"世界模型"？ 智驾的学术领袖和未来的具身学术领袖，Un ...

Core Insights - The article discusses the development and effectiveness of SSRL (Structured Search Reinforcement Learning) in enhancing the training efficiency and stability of Search Agents using large language models (LLMs) [6][28] - SSRL demonstrates superior performance over traditional methods that rely on external search engines, achieving effective transfer from simulation to real-world applications (Sim2Real) [6][28] Group 1 - SSRL utilizes structured prompts and format rewards to effectively extract world knowledge from models, leading to improved performance across various benchmarks and reduced hallucination [2][6] - The research highlights the high costs and inefficiencies associated with current RL training methods for Search Agents, which include full-real and semi-real search approaches [7][13] - The introduction of SSRL allows for a significant increase in training efficiency, estimated at approximately 5.6 times, while maintaining a continuous increase in training rewards without collapse [31][32] Group 2 - Experiments show that models trained with SSRL outperform those relying on external engines, particularly in real-world search scenarios, indicating the importance of integrating real-world knowledge [28][31] - The article presents findings that suggest the combination of self-generated knowledge and real-world knowledge can enhance model performance, particularly through entropy-guided search strategies [34] - The integration of SSRL with TTRL (Task-Driven Reinforcement Learning) has shown to improve generalization and effectiveness, achieving up to a 67% performance increase in certain tasks [38][39]

Group 1 - The core issue in embodied intelligence is the severe shortage of real data, with most robotic models relying on less than 1% of real operational data, which limits their generalization capabilities in complex environments [5][6] - There is a debate in the industry regarding the importance of real data versus synthetic simulation data, which affects the scalability and generalization of embodied intelligence [6][7] - Some experts argue that while synthetic data has advantages in cost and scalability, it cannot fully replicate the complexities of the real world, leading to a "domain gap" that hinders model transferability [7][8] Group 2 - The need for hundreds of billions of real data points is highlighted, with current datasets only reaching the million level, presenting a significant bottleneck for the development of embodied intelligence [8] - The strategy of using synthetic data for initial training followed by fine-tuning with real data is seen as a key pathway for the cold start and scaling of embodied intelligence [8][9] - Teleoperation is emerging as a primary method for acquiring real data, especially in the early stages of embodied intelligence, where human operators provide high-quality demonstration actions for training [9][10]

Core Insights - The 27th Annual Conference of the China Association for Science and Technology (CAST) was held in Beijing from July 1 to 31, focusing on "Tracing Technological Frontiers to Support Innovative Development" [1] - The conference attracted over 7,000 participants, including more than 110 academicians, with 57% of attendees being young scientists under 40 years old [1] - A total of over 990 high-level academic reports were presented during the conference [1] Group 1 - One highlight of the conference was the deep involvement and leadership of young scientists in frontier discussions [2] - The conference fostered an atmosphere of equal communication, allowing young scholars to directly question academicians and experts, which is beneficial for breaking cognitive biases and enhancing problem understanding [4] - The design of forums encouraged embracing uncertainty in research and promoted non-consensus viewpoints, creating a more inclusive and open academic environment [4] Group 2 - Participants engaged in discussions on cutting-edge topics such as "Sim2Real challenges" and "multimodal perception fusion" in the "Embodied Intelligent Robots" forum, inspiring new research directions [3] - The "Key Technologies for Commercialization of Controlled Nuclear Fusion" forum attracted diverse participants from academia, industry, and technology sectors, facilitating in-depth discussions on uncertain topics related to nuclear fusion [4] - The conference emphasized the importance of fostering divergent thinking and academic innovation through collaborative discussions among participants of varying expertise and age [4]

Core Viewpoint - Kuawei Intelligent, a leading domestic embodied intelligent robot company, has completed several hundred million yuan in A1 and A2 round financing, with plans to increase investment in technology research and product innovation [1][3]. Financing History - On July 10, 2025, Kuawei Intelligent completed A1 and A2 round financing, led by Chengdu Kechuang Investment and Hongtai Fund, with participation from various well-known institutions [2]. - The company had previously completed a Pre-A+ round financing on January 16, 2025, with undisclosed amounts [2]. - Kuawei Intelligent has completed a total of eight financing rounds, including angel and Pre-A rounds, with significant investments from various venture capital firms [2]. Company Overview - Kuawei Intelligent was established on June 15, 2021, in Shenzhen, initially focusing on 3D vision software and hardware product development [4]. - The company has evolved to focus on developing highly versatile embodied intelligent technology, positioning itself as a national high-tech enterprise [5]. Product Launch - On January 20, 2025, Kuawei Intelligent launched the DexForce W1, the industry's first humanoid robot based on the Sim2Real embodied intelligent engine [8][9]. - The W1 robot features a height of 170 cm, a maximum arm load of 20 kg, and an 8-hour battery life, with capabilities for autonomous charging and folding [11]. Technological Advancements - The DexVerse embodied intelligent engine enables rapid deployment and application switching in various environments, enhancing the robot's operational capabilities [11][12]. - Kuawei Intelligent has developed a comprehensive product matrix that includes the embodied intelligent brain, sensors, and general-purpose robots [12][14]. Commercialization Strategy - Initially focused on industrial robots, Kuawei Intelligent has shifted its focus to embodied large models and simulation data, emphasizing the end-to-end capabilities of the DexVerse engine [16]. - The company has achieved significant commercial success, with annual revenue reaching the billion yuan level and serving over a hundred clients across various industries [16]. Industry Context - The humanoid robot sector is divided into two main camps: hardware-focused companies like Yushu Technology and software-driven companies like ZhiYuan Robotics [17]. - Kuawei Intelligent is positioned in the software camp, emphasizing the importance of strong AI capabilities for commercialization in the humanoid robot market [17][19].

Core Viewpoint - The article discusses the unprecedented advancements in AI, particularly in embodied intelligence, which is transforming the relationship between humans and machines. Major tech companies are competing in this revolutionary field, which has the potential to significantly impact various industries such as manufacturing, healthcare, and space exploration [1][2]. Group 1: Embodied Intelligence - Embodied intelligence is characterized by machines that can understand language commands, navigate complex environments, and make intelligent decisions in real-time [1]. - Leading companies like Tesla, Boston Dynamics, OpenAI, and Google are actively developing technologies in this area, emphasizing the need for AI systems to possess both a "brain" and a "body" [1][2]. Group 2: Technical Challenges - Achieving true embodied intelligence presents significant technical challenges, including the need for advanced algorithms and a deep understanding of physical simulation, robot control, and perception fusion [2][4]. - MuJoCo (Multi-Joint dynamics with Contact) is highlighted as a key technology in overcoming these challenges, serving as a high-fidelity training environment for robot learning [4][6]. Group 3: MuJoCo's Role - MuJoCo is not just a physics simulation engine; it acts as a crucial bridge between the virtual and real worlds, enabling researchers to conduct millions of trials in a simulated environment without risking expensive hardware [4][6]. - The advantages of MuJoCo include simulation speeds hundreds of times faster than real-time, the ability to test extreme scenarios safely, and effective transfer of learned strategies to real-world applications [6][8]. Group 4: Educational Opportunities - A comprehensive MuJoCo development course has been created, focusing on practical applications and theoretical foundations, covering topics from physics simulation to deep reinforcement learning [9][10]. - The course is structured into six modules, each with specific learning objectives and practical projects, ensuring a solid grasp of embodied intelligence technologies [11][13]. Group 5: Project-Based Learning - The course includes six progressively challenging projects, such as building a robotic arm control system and implementing vision-guided grasping, which are designed to reinforce theoretical concepts through hands-on experience [15][17][19]. - Each project is tailored to address specific technical points while aligning with overall learning goals, providing a comprehensive understanding of embodied intelligence [12][28]. Group 6: Career Development - Completing the course equips participants with a complete skill set in embodied intelligence, enhancing their technical, engineering, and innovative capabilities, which are crucial for career advancement in this field [29][31]. - Potential career paths include roles as robot algorithm engineers, AI research engineers, or product managers, with competitive salaries ranging from 300,000 to 1,500,000 CNY depending on the position and company [33].

Core Insights - The article discusses the unprecedented turning point in AI development, highlighting the rise of embodied intelligence, which allows machines to understand language, navigate complex environments, and make intelligent decisions [1][2]. Group 1: Embodied Intelligence - Embodied intelligence is defined as AI systems that not only possess a "brain" but also have a "body" capable of perceiving and interacting with the physical world [1]. - Major tech companies like Tesla, Boston Dynamics, OpenAI, and Google are competing in this transformative field, which is expected to revolutionize various industries including manufacturing, healthcare, and space exploration [1]. Group 2: Technical Challenges - Achieving true embodied intelligence faces significant technical challenges, requiring advanced algorithms and a deep understanding of physical simulation, robot control, and perception fusion [2][4]. - MuJoCo (Multi-Joint dynamics with Contact) is identified as a key technology in this domain, serving as a high-fidelity training environment for robot learning [4][8]. Group 3: MuJoCo's Role - MuJoCo allows researchers to create realistic virtual robots and environments, enabling millions of trials and learning experiences without the risk of damaging expensive hardware [6][4]. - The simulation speed can be hundreds of times faster than real-time, significantly accelerating the learning process [6]. - MuJoCo has become a standard tool in both academia and industry, with major companies utilizing it for robot research [8]. Group 4: Practical Training - A comprehensive MuJoCo development course has been designed, focusing on practical applications and theoretical foundations, covering topics from physical simulation to deep reinforcement learning [9][10]. - The course is structured into six modules, each with specific learning objectives and practical projects, ensuring a solid grasp of the technology stack [13][16]. Group 5: Project-Based Learning - The course includes six progressively challenging projects, such as building a robotic arm control system and implementing vision-guided grasping [19][21]. - Each project is designed to reinforce theoretical concepts through hands-on experience, ensuring participants understand both the "how" and "why" of the technology [29][33]. Group 6: Target Audience and Outcomes - The course is suitable for individuals with programming or algorithm backgrounds looking to enter the field of embodied robotics, as well as students and professionals interested in enhancing their practical skills [30][32]. - Upon completion, participants will have a complete technology stack in embodied intelligence, gaining advantages in technical, engineering, and innovation capabilities [32][33].

以下文章来源于晚点Auto ，作者晚点团队 文丨王与桐 晚点Auto . 编辑丨程曼祺 从制造到创造，从不可能到可能。《晚点LatePost》旗下汽车品牌。 今年 5 月，一批身高 1.72 米的新工人来到美国得州工厂上班，他们负责把一粒粒圆柱形的 4680 电芯从传 输台上码放到面前的红色盒子里。他们不算熟练，甚至动作迟缓、笨手笨脚。但这批工人是 Optimus，特 斯拉 2022 年发布的人形机器人，一切不一样了。 "完美的使用场景""进步神速""失业警告"，在特斯拉释放的机器人工作视频下，人们的评论有惊叹，有担 忧。 银河通用的 Galbot 在捡垃圾，它没有双腿，而是可折叠的单腿 + 轮式底盘。 获取足够多的数据是具身智能发展的难点，特斯拉、Google 都选择用 "遥操" 采集数据，即让真人戴上一 些采集设备来完成机器人要学的动作。王鹤觉得这样算不过账："Google 做十几万条数据，就用了十多个 月、花费巨大。" 银河通用选择 all in "Sim2Real（从仿真到真机的迁移）"，即主要依靠合成仿真数据。 美国的人形机器人公司钱多、胆大，王鹤的一个观察是，这让他们没有严格地寻找 PMF（Pro ...