Core Viewpoint - End-to-end (E2E) autonomous driving is currently the core algorithm for mass production in intelligent driving, with significant advancements in the VLA (Vision-Language Architecture) and VLM (Vision-Language Model) systems, leading to high demand for related positions in the industry [2][4]. Summary by Sections Section 1: Background Knowledge - The course aims to provide a comprehensive understanding of end-to-end autonomous driving, including its historical development and the transition from modular to end-to-end approaches [21]. - Key technical stacks such as VLA, diffusion models, and reinforcement learning are essential for understanding the current landscape of autonomous driving technology [22]. Section 2: Job Market Insights - Positions related to VLA/VLM algorithms offer lucrative salaries, with 3-5 years of experience earning between 40K to 70K monthly, and top talents in the field can earn up to 1 million annually [10]. - The demand for VLA-related roles is increasing, indicating a shift in the industry towards advanced model architectures [9]. Section 3: Course Structure - The course is structured into five chapters, covering topics from basic concepts of end-to-end algorithms to advanced applications in VLA and reinforcement learning [19][30]. - Practical components are included to bridge the gap between theory and application, ensuring participants can implement learned concepts in real-world scenarios [18]. Section 4: Technical Innovations - Various approaches within end-to-end frameworks are explored, including two-stage and one-stage methods, with notable models like PLUTO and UniAD leading the way [4][23]. - The introduction of diffusion models has revolutionized trajectory prediction, allowing for better adaptability in uncertain driving environments [24]. Section 5: Learning Outcomes - Participants are expected to achieve a level of proficiency equivalent to one year of experience as an end-to-end autonomous driving algorithm engineer, mastering key technologies and frameworks [32]. - The course emphasizes the importance of understanding BEV perception, multimodal models, and reinforcement learning to stay competitive in the evolving job market [32].

Core Viewpoint - End-to-End Autonomous Driving (E2E) is identified as the core algorithm for intelligent driving mass production, marking a significant shift in the industry towards more integrated and efficient systems [2][4]. Group 1: Technology Overview - E2E can be categorized into single-stage and two-stage approaches, with the latter gaining traction following the recognition of UniAD at CVPR [2]. - The E2E system directly models the relationship between sensor inputs and vehicle control information, minimizing errors associated with modular approaches [2]. - The introduction of BEV perception has bridged gaps between modular methods, leading to a technological leap in the field [2]. Group 2: Challenges in Learning - The rapid development of E2E technology has made previous educational resources outdated, creating a need for updated learning materials [5]. - The fragmented nature of knowledge across various domains complicates the learning process for newcomers, often leading to abandonment before mastery [5]. - A lack of high-quality documentation in E2E research increases the difficulty of entry into the field [5]. Group 3: Course Development - A new course titled "End-to-End and VLA Autonomous Driving" has been developed to address the challenges faced by learners [6]. - The course aims to provide a quick entry into core technologies using accessible language and examples, facilitating easier expansion into specific knowledge areas [6]. - It focuses on building a framework for understanding E2E research and enhancing research capabilities by categorizing papers and extracting innovative points [7]. Group 4: Course Structure - The course is structured into several chapters, covering topics from the history and evolution of E2E algorithms to practical applications and advanced techniques [11][12][20]. - Key areas of focus include the introduction of E2E algorithms, background knowledge on relevant technologies, and detailed explorations of both single-stage and two-stage methods [11][12][20]. - Practical components are integrated into the curriculum to ensure a comprehensive understanding of theoretical concepts [8]. Group 5: Expected Outcomes - Participants are expected to achieve a level of proficiency equivalent to one year of experience as an E2E autonomous driving algorithm engineer [27]. - The course will cover a wide range of methodologies, including single-stage, two-stage, world models, and diffusion models, providing a holistic view of the E2E landscape [27]. - A deeper understanding of key technologies such as BEV perception, multimodal large models, and reinforcement learning will be developed [27].

Core Viewpoint - The article discusses the differences between VLA (Vision-Language-Action) and end-to-end models in the context of autonomous driving, emphasizing the importance of large models and their applications in the industry [2]. Group 1: Job Descriptions and Requirements - Positions related to large model development, including VLA and end-to-end roles, are highlighted, with a focus on skills in fine-tuning, lightweight models, and deployment [2]. - The job of an end-to-end/VLA engineer involves developing and implementing driving systems, optimizing model structures, and constructing high-quality training datasets [6]. - The VLA/VLM algorithm position requires a master's degree in computer science or AI, with 3-5 years of experience in autonomous driving or AI algorithms, and proficiency in VLA/VLM architectures [8][10]. Group 2: Technical Skills and Experience - Candidates are expected to have experience with multimodal large language models, fine-tuning existing models for specific business scenarios, and familiarity with Transformer and multimodal technologies [5]. - Experience in computer vision, trajectory prediction, and decision planning is essential, along with a strong foundation in mainstream technologies and frameworks like PyTorch [9]. - The article emphasizes the need for candidates to have published papers in top conferences or achieved notable results in international competitions [9][11].

Core Insights - The article provides a comprehensive overview of various surveys and research papers related to embodied intelligence, focusing on areas such as vision-language-action models, reinforcement learning, and robotics applications [1][2][3][4][5][6][7][8][9] Group 1: Vision-Language-Action Models - A survey on Vision-Language-Action (VLA) models highlights their significance in autonomous driving and human motor learning, discussing progress, challenges, and future trends [2][3][8] - The exploration of VLA models emphasizes their applications in embodied AI, showcasing various datasets and methodologies [8][9] Group 2: Robotics and Reinforcement Learning - Research on foundation models in robotics addresses applications, challenges, and future directions, indicating a growing interest in integrating AI with robotic systems [3][4] - Deep reinforcement learning is identified as a key area with real-world successes, suggesting its potential for enhancing robotic capabilities [3] Group 3: Multimodal and Generative Approaches - The article discusses multimodal fusion and vision-language models, which are crucial for improving robot vision and interaction with the environment [6] - Generative artificial intelligence in robotic manipulation is highlighted as an emerging field, indicating a shift towards more sophisticated AI-driven robotic systems [6] Group 4: Datasets and Community Engagement - The article encourages engagement with a community focused on embodied intelligence, offering access to a wealth of resources, including datasets and collaborative projects [9]

Core Viewpoint - End-to-End Autonomous Driving (E2E) is the core algorithm for intelligent driving mass production, marking a new phase in the industry with significant advancements and competition following the recognition of UniAD at CVPR [2] Group 1: E2E Autonomous Driving Overview - E2E can be categorized into single-stage and two-stage approaches, directly modeling from sensor data to vehicle control information, thus avoiding error accumulation seen in modular methods [2] - The emergence of BEV perception has bridged gaps between modular methods, leading to a significant technological leap [2] - The rapid development of E2E has led to a surge in demand for VLM/VLA expertise, with potential salaries reaching millions annually [2] Group 2: Learning Challenges - The fast-paced evolution of E2E technology has made previous learning materials outdated, necessitating a comprehensive understanding of multi-modal large models, BEV perception, reinforcement learning, and more [3] - Beginners face challenges in synthesizing knowledge from numerous fragmented papers and transitioning from theory to practice due to a lack of high-quality documentation [3] Group 3: Course Development - A new course titled "End-to-End and VLA Autonomous Driving" has been developed to address learning challenges, focusing on Just-in-Time Learning to help students quickly grasp core technologies [4] - The course aims to build a framework for research capabilities, enabling students to categorize papers and extract innovative points [5] - Practical applications are integrated into the course to ensure a complete learning loop from theory to practice [6] Group 4: Course Structure - The course consists of multiple chapters covering the history and evolution of E2E algorithms, background knowledge, two-stage and one-stage E2E methods, and the latest advancements in VLA [8][9][10] - Key topics include the introduction of E2E algorithms, background knowledge on VLA, and practical applications of diffusion models and reinforcement learning [11][12] Group 5: Target Audience and Outcomes - The course is designed for individuals with a foundational understanding of autonomous driving and aims to elevate participants to a level comparable to one year of experience as an E2E algorithm engineer [19] - Participants will gain a deep understanding of key technologies such as BEV perception, multi-modal large models, and reinforcement learning, enabling them to apply learned concepts to real-world projects [19]

Core Insights - The article highlights the key research directions in computer vision and autonomous driving as presented at major conferences CVPR and ICCV, focusing on four main areas: general computer vision, autonomous driving, embodied intelligence, and 3D vision [2][3]. Group 1: Research Directions - In the field of computer vision and image processing, the main research topics include diffusion models, image quality assessment, semi-supervised learning, zero-shot learning, and open-world detection [3]. - Autonomous driving research is concentrated on end-to-end systems, closed-loop simulation, 3D ground segmentation (3DGS), multimodal large models, diffusion models, world models, and trajectory prediction [3]. - Embodied intelligence focuses on visual language navigation (VLA), zero-shot learning, robotic manipulation, end-to-end systems, sim-to-real transfer, and dexterous grasping [3]. - The 3D vision domain emphasizes point cloud completion, single-view reconstruction, 3D ground segmentation (3DGS), 3D matching, video compression, and Neural Radiance Fields (NeRF) [3]. Group 2: Research Support and Collaboration - The article offers support for various research needs in autonomous driving, including large models, VLA, end-to-end autonomous driving, 3DGS, BEV perception, target tracking, and multi-sensor fusion [4]. - In the embodied intelligence area, support is provided for VLA, visual language navigation, end-to-end systems, reinforcement learning, diffusion policy, sim-to-real, embodied interaction, and robotic decision-making [4]. - For 3D vision, the focus is on point cloud processing, 3DGS, and SLAM [4]. - General computer vision support includes diffusion models, image quality assessment, semi-supervised learning, and zero-shot learning [4].

Core Insights - The embodied intelligence sector is experiencing unprecedented investment and interest, with discussions on whether there is a bubble and which applications will mature first [1][3] Investment Landscape - The current investment scale in embodied intelligence is significantly lower than that in the smart automotive sector, indicating potential for growth once scalable commercial applications are identified [3][4] - Companies believe that more capital is needed to bridge the financing gap between domestic and international players, with domestic leading companies operating at a scale of tens of billions of RMB compared to tens of billions of USD for their US counterparts [3][4] Market Applications - B-end applications are seen as the most suitable for initial deployment, particularly in areas like logistics, quality inspection, and manufacturing processes [6][7] - The industry is exploring various strategies, including the replacement of human labor in hard-to-fill positions, with a gradual expansion into more complex scenarios over the next few years [6][7] Technological Development - The VLA (Vision, Language, Action) model is considered a key framework for the future of robotics, with ongoing improvements in data collection and model training methodologies [7][8] - The industry is moving towards a unified model paradigm, emphasizing the importance of integrating visual, linguistic, and action capabilities in robotic systems [8] Competitive Landscape - The embodied intelligence sector is expected to evolve similarly to the smartphone and automotive industries, with a diverse range of players including hardware manufacturers and AI developers [9][10] - The market is anticipated to consolidate into a limited number of major players, with a focus on maintaining technological barriers and establishing closed-loop commercial applications [10][11]

" 互联网视频数据是唯一可以 scale up 的道路 。 " 作者丨 郭海惟 编辑丨 陈彩娴 作为一名具身大脑的创业者，卢宗青有着金光闪闪的履历： 他是紧随 DeepMind之后，中国新生代的强化学习研究者。北京大学计算机学院长聘副教授，担任过智源 研究院多模态交互研究中心负责人，负责过首个国家自然科学基金委原创探索计划通用智能体项目，还同 时在NeurIPS、ICLR、ICML等机器学习的国际顶级会议担任领域主席。 早在 2023年，他旗下团队便有利用多模态模型研究通用 Agent 的研究尝试，让 Agent 玩《荒野大镖客 2》和办公，使其成为第一个从零开始在AAA级游戏中完成具体任务的 LLM 智能体。相关论文几经波折， 今年终于被 ICML 2025 录用。不过他自述对那份研究其实不够满意，因为"泛化性不足"。 当完成那些研究以后，卢宗青意识到 "当前的多模态模型缺乏与世界交互的能力"。因为模型缺少学习物 理交互的数据，所以 我们看到的那些泛化的能力本质都是 "抽象"的，它终究无法理解动作和世界的关 系，自然也无法预测世界 。 这如今成为他想在具身智能创业的起点：开发一个通用的具身人工智能模型。 卢 ...

Core Viewpoint - The article discusses the advancements in embodied intelligence, particularly focusing on Lingchu Intelligent's development of the Psi R1 model, which enables robots to perform complex tasks in dynamic environments, such as playing Mahjong with humans [3][6][17]. Company Overview - Lingchu Intelligent was founded in 2024 by a team with extensive experience in robotics and artificial intelligence, including CEO Wang Qibin, who has a background in product management, and other notable figures from Stanford University and the robotics field [5][6]. - The company has established a joint laboratory with Peking University to enhance its research capabilities in embodied intelligence [5]. Technology and Innovation - The Psi R1 model represents a significant advancement in robot capabilities, allowing for "action perception-environment feedback-dynamic decision-making" in a closed-loop system [3][6]. - The transition from Vision Language Models (VLM) to Vision Language Action Models (VLA) is highlighted, with VLA enabling robots to understand and execute physical actions based on visual and textual information [7][14]. - The company aims to address the challenges of long-range operations in semi-open environments, which are crucial for practical applications in logistics and retail [8][14]. Market Position and Strategy - Lingchu Intelligent positions itself as a provider of stable and cost-effective robotic solutions, focusing on practical applications rather than superficial demonstrations [5][10]. - The company has plans to deliver products to overseas logistics clients within six months, indicating a clear market strategy [7][21]. - The target market includes manufacturing processes and logistics operations, with a focus on tasks such as material inspection and handling [21]. Financial Outlook - The company anticipates achieving sales of several hundred million by the end of 2026, reflecting a strong growth trajectory [22]. - Pricing strategies are designed to be competitive, aiming to keep robot costs below two years' worth of labor costs for similar positions [23]. Industry Trends - There is a growing expectation from investors for clear commercialization pathways in the field of embodied intelligence, contrasting with previous years [8][25]. - The article notes that while there is significant investment in the sector, the focus is shifting towards sustainable and viable technological advancements [25][26].

GALBOT G1正在抓取商品 图片来源：每经记者 李宇彤 摄 每经记者｜李宇彤 每经编辑｜马子卿 "我觉得今天我们谈具身智能，它有一个当下的目标，就是我们一定要推动具身智能的产业化。"6月6日，在"2025智源大会"上，北京银河通用机器人有限公 司（以下简称"银河通用"）的创始人兼CTO（首席技术官）王鹤在会上如是说道。 而银河通用的轮式双臂机器人GALBOT G1也亮相现场。演示环节中，GALBOT G1在听到指令后，开始准确地从现场搭建的商品摆放密集的货架上，抓取 对应的物品。 银河通用创始人兼CTO 王鹤 图片来源：每经记者 李宇彤 摄 2023年5月，银河通用在北京海淀创立，公司专注研发人形机器人硬件和具身智能大模型。在过去一年多时间里就完成了超12亿元融资，投资方既包括美团 战投、北汽产投、商汤国香基金等战略及产业投资方，也包括启明创投、蓝驰创投、IDG资本等明星机构。 6月1日，银河通用正式推出自主研发的产品级端到端导航大模型TrackVLA。这是一款具备纯视觉环境感知、语言指令驱动、可自主推理、具备零样本 （Zero-Shot）泛化能力的具身大模型。 在银河通用发布的演示短片中，机器狗在大模型 ...