Core Viewpoint - The article discusses the current trends and opportunities in the fields of autonomous driving and embodied intelligence, emphasizing the need for strong technical skills and knowledge in cutting-edge technologies for job seekers in these areas [3][4]. Group 1: Job Market Insights - The job market for autonomous driving and embodied intelligence is competitive, with a high demand for candidates with strong backgrounds and technical skills [2][3]. - Companies are increasingly looking for expertise in advanced areas such as end-to-end models, visual language models (VLM), and reinforcement learning [3][4]. - There is a saturation of talent in traditional robotics, but many startups in the robotics sector are rapidly growing and attracting significant funding [3][4]. Group 2: Learning and Development - The article encourages individuals to enhance their technical skills, particularly in areas like SLAM (Simultaneous Localization and Mapping) and ROS (Robot Operating System), which are relevant to robotics and embodied intelligence [3][4]. - A community platform is mentioned that offers resources such as video courses, hardware learning materials, and job information, aiming to build a large network of professionals in intelligent driving and embodied intelligence [5]. Group 3: Technical Trends - The article highlights four major technical directions in the industry: visual language models, world models, diffusion models, and end-to-end autonomous driving [8]. - It provides links to various resources and papers related to these technologies, indicating a focus on the latest advancements and applications in the field [9][10].

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].

Company Research - The company is recognized as a global leader in collaborative robots, with its commercialization capabilities expected to continue validating its market position [3] - The company possesses a globally leading technological barrier, with a fully self-developed ecosystem that establishes a competitive moat, laying the foundation for future development and cost reduction [3] - The company's global layout has shown significant results, benefiting from the manufacturing industry's transition [3] - The company is actively entering the fields of embodied intelligence and humanoid robots, which opens up long-term growth opportunities [3]

Core Viewpoint - The article emphasizes the importance of spatial intelligence in achieving Artificial General Intelligence (AGI), as articulated by AI pioneer Fei-Fei Li, who believes that understanding and interacting with the 3D world is fundamental to AI development [2][29]. Group 1: Spatial Intelligence and AGI - Fei-Fei Li asserts that without spatial intelligence, AGI is incomplete, highlighting the necessity of creating world models that capture the structure and dynamics of the 3D world [29][33]. - The understanding of 3D world modeling is deemed crucial for AI, involving tasks such as reasoning, generating, and acting within a three-dimensional context [8][33]. Group 2: ImageNet and Its Impact - The creation of ImageNet was a pivotal moment in AI, providing a large dataset that enabled significant advancements in computer vision and machine learning [12][18]. - ImageNet's challenge established benchmarks for object recognition, leading to breakthroughs in algorithms, particularly with the introduction of convolutional neural networks like AlexNet [19][24]. Group 3: Evolution of AI and Future Directions - The conversation reflects on the evolution of AI from object recognition to scene understanding and now to generative models, indicating a rapid progression in capabilities [31][27]. - Fei-Fei Li expresses excitement about the potential of generative AI and its applications in various fields, including design, gaming, and robotics, emphasizing the need for robust world models [41][42]. Group 4: Challenges in Spatial Intelligence - A significant challenge in developing spatial intelligence is the lack of accessible spatial data compared to the abundance of language data available online [36][73]. - The complexity of understanding and modeling the 3D world is highlighted, as it involves intricate interactions and adherence to physical laws, making it a more challenging domain than language processing [35][39]. Group 5: Personal Insights and Experiences - Fei-Fei Li shares her journey from academia to entrepreneurship, emphasizing the importance of curiosity and a fearless mindset in tackling difficult problems [46][55]. - The article concludes with encouragement for young researchers to pursue their passions and embrace challenges, reflecting on the transformative nature of AI and its potential to benefit humanity [77].

点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近15个 方向 学习 路线 世界模型Epona 地平线、清华、北大等团队ICCV'25中稿的自回归扩散世界模型工作，同时可以不依赖视频预测独立输出轨 迹规划。 主要贡献： 论文标题：Epona: Autoregressive Diffusion World Model for Autonomous Driving 论文链接：https://arxiv.org/abs/2506.24113 项目主页：https://kevin-thu.github.io/Epona/ 长时序生成。Epona可以实现长达2分钟的长时间生成，显著优于现有的世界模型； 实时轨迹规划。独立的多模态生成架构能够在视频预测不可用的情况下独立输出轨迹规划，从而显著降 低了推理FLOPS。这实现了高质量甚至实时的轨迹规划，高达20Hz的帧率； 视觉细节的保存。Epona的自回归公式采用连续视觉标记器而不是离散标记器，从而保留了丰富的场景 细节； 可视化： 算法框架： 实验结果： | Metric | | | | DriveGAN [30] DriveDreamer [5 ...

与其讨论同样的idea别人为什么能中顶会，不如讨论在同样的idea下顶会的论文究竟强在哪里？ 1. 是否为一个point solution？ 同样的idea ，如果单纯把某些指标刷的很高那多半中不了顶会。那就是point solution，本身而言不具备太大的影响力。 顶会的成果，绝大部分不单纯只 能用在某个特定的地方，这至少一个系列的方法。 那么对于想要快速有科研成果的小伙伴来说， 最重要的问题莫过于如何能高效、精准、短平快地中稿，特别是中稿顶会。 在前沿且复杂的自动驾驶、具 身智能、机器人领域，没有专业的领路人发顶会真的很难！ 为此，我们为有需要的小伙伴推出了深度辅导，面向计算机全领域及AI4s领域，提升论文中稿率，直至拿下顶会！ 能中的文章才是好文章， 咨询更多扫码添加： 适用人群 我们能提供什么？ 2. 文章的方法实现起来是否困难？ 同样的idea，但是别人的论文实现无难度，效果还杠杠的；或者实现起来虽然很复杂，但是使用起来很容易，这样的论文不中什么样的论文中? 从idea、实验设计、数据集选择、跑通baseline最后到初稿的写作， 任何一个环节的细微差别都会导致最后投稿区位的巨大不同。 清晰的科研 ...

Core Viewpoint - The article discusses the advancements in AI technology, particularly in autonomous driving and embodied intelligence, highlighting the saturation of the autonomous driving industry and the challenges faced by job seekers in this field [2]. Group 1: Industry Developments - The autonomous driving sector has seen significant breakthroughs, with L2 to L4 functionalities being mass-produced, alongside advancements in humanoid robots and quadrupedal robots [2]. - The industry has a clear demand for technology and talent, as evidenced by the experiences shared by job seekers [2]. Group 2: Job Seeking Platform - The introduction of AutoRobo Knowledge Community aims to assist job seekers in the fields of autonomous driving, embodied intelligence, and robotics, providing a platform for job matching and networking [2][3]. - The community currently has nearly 1,000 members, including professionals from companies like Horizon Robotics, Li Auto, Huawei, and Xiaomi [2]. Group 3: Resources and Support - The community offers a variety of resources, including interview questions, industry reports, salary negotiation tips, and resume optimization services [3][4]. - Specific interview preparation materials include a compilation of 100 questions related to autonomous driving and embodied intelligence, covering various technical aspects [6][7][11]. Group 4: Industry Reports - The community provides access to numerous industry reports that help members understand the current state, development trends, and market opportunities within the autonomous driving and embodied intelligence sectors [12][15].

在人工智能与机器人技术加速迭代的当下，自动驾驶算法正从虚拟测试走向真实道路与物流运输场景落地，专 业级机械臂也逐渐成为课堂示教编程的得力助手。随着行业发展，传统基础编程实践已难以满足需求，科研人 员与开发者更热衷于在仿真环境中钻研多机协同算法等前沿课题；与此同时，实验室对机器人在模拟场景下， 实现测试识别、精准抓取、智能避障、高效搬运全链路流程的要求也水涨船高。 在一背景下， 大象机器人于近日推出移动复合机器人 myAGV Pro， 为智能机器人领域的探索与实践带来新 的解决方案。 据机器人大讲堂了解，myAGV Pro是 大象机器人 旗下的 全向转向系统小车 ，适配大象机器人旗下轻量化 协作机械臂生态和市面上大部分通用协作机器人，能够完成 多目标环境检测、语音语义识别 等人工智能的相 关应用。大象机器人推出该产品主要旨在为研究人员与开发者在智能机器人领域进行 仓储搬运、教学学习、 创新应用、科研研究和竞技比赛 等场景提供理想平台。 myAGV Pro 中文宣传片 3 丰富生态，支持二次开发 原 生 搭 载 Ubuntu 22.04 ， 提 供 RVIZ 、 Gazebo 仿 真 环 境 支 持 。 同 时 ...

Group 1 - The total number of private economic organizations in China has steadily increased, reaching 185 million by May 2025, accounting for 96.76% of all operating entities, with a year-on-year growth of 2.3% [2][3] - Private enterprises exceed 58 million, showing a year-on-year increase of 5.2%, while individual businesses reach 127 million, with a growth of 1.0% [3][5] - Private enterprises are focusing on "new quality productivity," significantly contributing to technological innovation in strategic emerging industries such as new energy and high-end equipment manufacturing [2][4] Group 2 - Private enterprises play a crucial role in driving industrial upgrades and stabilizing economic growth, with significant investments in technology and innovation [3][4] - Notable private companies like Huawei and BYD are leading in their respective fields, with Huawei's R&D expenditure reaching 179.7 billion yuan in 2024, accounting for 20.8% of its total revenue [4][5] - Individual businesses are vital for community economies, with 127 million individual businesses providing over 250 million jobs, significantly contributing to employment stability [5][6] Group 3 - The flexibility and market sensitivity of small and medium-sized enterprises enable them to quickly adapt to market demands and convert technological innovations into productive forces [5][6] - Individual businesses are increasingly adopting digital tools to enhance their operations, integrating online and offline services to expand their customer base [6]

Core Viewpoint - The article emphasizes the importance of advanced skills and knowledge in the fields of autonomous driving and embodied intelligence, highlighting the need for candidates with strong backgrounds to meet industry demands. Group 1: Industry Trends - The demand for talent in autonomous driving and embodied intelligence is increasing, with a focus on cutting-edge technologies such as SLAM, ROS, and large models [3][4]. - Many companies are transitioning from traditional methods to more advanced techniques, indicating a shift in the required skill sets for job seekers [3][4]. - The article notes that while there is a saturation of talent in certain areas, the growth of startups in robotics presents new opportunities for learning and development [3][4]. Group 2: Learning and Development - The article encourages individuals to enhance their technical skills, particularly in areas related to robotics and embodied intelligence, which are seen as the forefront of technology [3][4]. - It mentions the availability of resources and community support for learning, including access to courses, hardware, and job information through platforms like Knowledge Planet [5][6]. - The community aims to create a comprehensive ecosystem for knowledge sharing and recruitment in the fields of intelligent driving and embodied intelligence [5][6]. Group 3: Technical Directions - The article outlines four major technical directions in the industry: visual large language models, world models, diffusion models, and end-to-end autonomous driving [7]. - It highlights the importance of staying updated with the latest research and developments in these areas, providing links to various resources and papers for further exploration [8][9].