Core Viewpoint - Nvidia is launching a comprehensive offensive in the autonomous driving sector with its open-source VLA model, Alpamayo, which aims to provide car manufacturers with a robust foundation for developing their own autonomous driving technologies [6][10][21]. Group 1: Nvidia's Innovations - At CES 2026, Nvidia announced the Alpamayo model, which utilizes a Vision-Language-Action (VLA) approach to enhance decision-making in autonomous driving by making the reasoning process interpretable and traceable [7][10]. - Alpamayo is the first open-source VLA model, allowing car manufacturers to customize it based on their data and needs, thus reducing development complexity while ensuring algorithmic differentiation [10][11]. - Alongside Alpamayo, Nvidia also introduced AlpaSim for closed-loop testing and the Physical AI dataset, which contains over 1,727 hours of driving data, providing a comprehensive toolkit for developers [11][13]. Group 2: Competitive Landscape - Other companies, such as Xiaopeng and Li Auto, are also developing VLA models, indicating a competitive shift towards this technology in the autonomous driving space [8][10]. - Tesla's FSD appears to be adopting a similar VLA-like architecture, although it remains less transparent compared to Nvidia's approach [10][14]. Group 3: Nvidia's Business Strategy - Nvidia's automotive business, while dominant in high-level driving assistance, has not met revenue expectations compared to its data center operations, prompting a strategic shift to provide more comprehensive support to car manufacturers [15][20]. - The company aims to create a closed-loop toolchain for intelligent driving, integrating cloud training and vehicle-side inference, thus facilitating easier adoption of its hardware and software solutions by automakers [21][22]. - Nvidia's strategy reflects a balance between standardization and customization, as it seeks to provide a rich software toolbox while avoiding direct involvement in specific autonomous driving projects [22][24].

Core Viewpoint - The article emphasizes the importance of deep learning and emerging technologies in the fields of automation and computer science, suggesting that students should focus on these areas to remain competitive in the job market [2]. Group 1: Recommended Learning Paths - For students in automation and computer science, deep learning, VLA, end-to-end systems, and world models are highlighted as promising areas with significant potential for research and career development [2]. - Mechanical and vehicle engineering students are advised to start with traditional PnC and 3DGS, which are easier to grasp and require lower computational power [2]. Group 2: Research Guidance Services - The article announces the launch of a paper guidance service that covers various advanced topics such as end-to-end systems, VLA, world models, reinforcement learning, and more [3]. - The service includes support for paper topic selection, full process guidance, experimental guidance, and doctoral application assistance [6][9]. Group 3: High Acceptance Rates - The guidance service boasts a high acceptance rate for papers, with several already published in top conferences and journals such as CVPR, AAAI, and ICLR [7]. - Different pricing structures are available based on the level of the paper, indicating a tailored approach to support [7].

Core Insights - The consensus among industry experts indicates that 2026 will be a pivotal year for the development of end-to-end (E2E) and VLA (Vision-Language Alignment) technologies in autonomous driving, with a focus on optimizing production processes rather than making significant algorithmic changes [1] - The industry is actively recruiting experienced algorithm engineers and developing talent to tackle the complex challenges ahead, particularly in areas such as BEV perception, large models, diffusion models, and reinforcement learning [1] Course Overview - The course on E2E and VLA autonomous driving is designed to provide a comprehensive learning path from principles to practical applications, developed in collaboration with industry leaders [3] - The course covers various aspects of E2E algorithms, including their historical development, advantages and disadvantages of different paradigms, and current trends in both academia and industry [6][7] - Key technical keywords that are expected to be frequently encountered in job interviews over the next two years are emphasized in the course content [7] Course Structure - Chapter 1 introduces the concept of E2E algorithms, discussing their evolution from modular approaches to current paradigms like VLA [6] - Chapter 2 focuses on the background knowledge necessary for understanding E2E technologies, including VLA, large language models, diffusion models, and reinforcement learning [11] - Chapter 3 delves into two-stage E2E algorithms, exploring their emergence and comparing them with one-stage approaches [7] - Chapter 4 presents one-stage E2E algorithms and VLA, highlighting various subfields and their contributions to achieving the ultimate goals of E2E systems [8] - Chapter 5 involves a practical assignment on RLHF (Reinforcement Learning from Human Feedback) fine-tuning, demonstrating how to build and experiment with pre-training and reinforcement learning modules [9] Learning Outcomes - The course aims to elevate participants to the level of an E2E autonomous driving algorithm engineer within approximately one year, covering a wide range of methodologies including one-stage, two-stage, world models, and diffusion models [15] - Participants will gain a deeper understanding of key technologies such as BEV perception, multimodal large models, reinforcement learning, and diffusion models, enabling them to apply their knowledge in real-world projects [15]

Core Viewpoint - The article discusses the challenges faced by beginners in the field of VLA (Vision-Language Alignment) tasks due to high costs and the complexity of data collection and model training, while introducing a comprehensive course aimed at addressing these issues and providing practical skills for aspiring professionals in the field [3][5][9]. Group 1: Challenges in VLA Tasks - Many students express frustration over the high costs associated with mechanical arms and sensors, which can exceed 15,000 yuan, making it difficult for self-learners or those without equipment to engage in VLA tasks [3]. - Open-source low-cost robotic arms are available, but many beginners struggle to achieve effective results due to difficulties in data collection and model training [4]. - A significant amount of time is wasted by students on troubleshooting and overcoming obstacles in data collection, model training, and deployment, particularly with complex models like π0 and π0.5, and GR00T [5]. Group 2: Course Offerings - The "Embodied Intelligence Heart" platform has replicated methods such as ACT, GR00T, π0, and π0.5 using SO-100 and LeRobot to help students who lack access to expensive equipment and do not know how to get started [8]. - A comprehensive VLA practical course has been developed in collaboration with industry experts, focusing on real-world applications and job readiness [9][14]. - The course covers a wide range of topics, including hardware for robotic arms, data collection, VLA algorithms, evaluation, simulation, deployment of mainstream VLA models, and various real-world experiments [14][15]. Group 3: Course Details and Requirements - Students who purchase the course will receive a SO-100 robotic arm, which includes both teaching and execution arms, delivered directly to them [18]. - The course is designed for individuals seeking practical experience and projects in the VLA field, including those transitioning from traditional computer vision, robotics, or autonomous driving [25]. - The course requires a foundational knowledge of Python and Pytorch, as well as experience in debugging real machines and data collection [25].

Core Viewpoint - The article discusses an advanced course on end-to-end (E2E) autonomous driving, focusing on the latest technologies such as BEV perception, Visual Language Models (VLM), diffusion models, and reinforcement learning, aimed at equipping participants with cutting-edge skills in the field [1][4][8]. Group 1: Course Structure - The course is divided into several chapters, starting with an introduction to end-to-end algorithms, covering the historical development and advantages of E2E methods over modular approaches [4]. - The second chapter focuses on background knowledge essential for understanding E2E technologies, including VLA, diffusion models, and reinforcement learning, which are crucial for job interviews in the next two years [5][9]. - The third chapter delves into two-stage E2E methods, discussing their emergence, advantages, and notable algorithms like PLUTO and CarPlanner [5][6]. - The fourth chapter highlights one-stage E2E methods and VLA, exploring various subfields and their contributions to achieving the ultimate goals of E2E systems [6][10]. Group 2: Practical Application - The course includes a major project on RLHF fine-tuning, allowing participants to apply their knowledge in practical scenarios, including building pre-training and reinforcement learning modules [7]. - The course aims to help participants reach a level equivalent to one year of experience as an E2E autonomous driving algorithm engineer, covering various methodologies and key technologies [13]. Group 3: Target Audience and Requirements - The course is designed for individuals with a foundational understanding of autonomous driving, familiar with basic modules, and concepts like transformer models, reinforcement learning, and BEV perception [11]. - Participants are expected to have a background in probability theory and linear algebra, as well as proficiency in Python and PyTorch [11].

Core Insights - The article emphasizes the importance of deep learning in the fields of automation and computer science, particularly for students in these areas to explore cutting-edge topics such as VLA, end-to-end learning, and world models [2][3] - It highlights the need for newcomers to engage with research papers and discussions to develop their own ideas and methodologies [2] - The article introduces a paper guidance service aimed at assisting students with various aspects of research paper writing and publication [3][4][6] Group 1 - The article suggests that students from computer science and automation backgrounds should focus on deep learning, with specific recommendations for topics like VLA, end-to-end learning, and world models [2] - For mechanical and vehicle engineering students, it recommends starting with traditional PnC and 3DGS due to their lower computational requirements and ease of entry [2] - The article encourages new researchers to learn from failures and emphasizes the importance of developing personal insights through extensive reading and communication [2] Group 2 - The paper guidance service offers support in selecting research topics, full process guidance, and experimental assistance [6] - The service has a high acceptance rate for papers submitted to top conferences and journals, including CVPR, AAAI, and ICLR [7] - Pricing for the guidance service varies based on the level of the paper, and further details can be obtained by contacting the research assistant [8]

搜狐科技《超级瓦力——对话具身智能50人》栏目第15期，对话灵初智能联合创始人陈源培。 划重点 1.VLA未必是终局，但现阶段效果最好。 2.无论合成数据还是仿真数据，都无法真正适配真实场景的复杂需求，最终还是要依赖真实数据，采集成本已做到马斯克团队的1/10。 3.中美模型差距没那么大，国内能追上，但中国的供应链和应用场景优势，美国短期难赶超。 出品｜搜狐科技 作者｜郑松毅 编辑｜杨锦 当多数人还将00后视为"新生代"时，这个群体已在具身智能赛道扛起大旗。出生于2001年的灵初智能联合创始人陈源培，正是这股年轻势力的典型代表。 从华南理工土木调剂生跨界RoboMaster全国冠军，从北大杨耀东门下的学术探索者到斯坦福 "AI 教母" 李飞飞的门徒，再到拒绝华为 "天才少年" 邀约、 以最年轻 AI 创业者身份跻身福布斯榜单，陈源培的每一步都在突破边界。 这位00后少年凭借超越年龄的行业清醒，将目光锁定灵巧手核心技术，在这家"科学家密度最高" 的具身智能创企，其团队将真实数据采集成本降至马斯 克公司的 1/10，破解了行业数据瓶颈。公司更是剑指2026年百万小时级全球最大灵巧手操作数据集，力求打造具身智能领域 ...

Group 1 - The core viewpoint of the article highlights the competitive landscape of the autonomous driving industry, emphasizing the focus on technology, cost, and efficiency as key areas of competition this year [1] - The industry has seen a shift with many professionals transitioning to sectors like embodied AI and drones, while autonomous driving remains a mature AI field, making algorithm talents highly sought after [1][2] - Major technological directions in autonomous driving have converged this year, including end-to-end systems, VLA, world models, and reinforcement learning, with many midstream companies tackling challenges like OCC and multi-sensor fusion perception [3] Group 2 - The membership of the paid community focused on autonomous driving has officially surpassed 4,000, indicating a growing interest in the development of technology routes and job information [3] - The company expresses gratitude to its supporters and announces various benefits and discounts for the new year, encouraging continued efforts in the upcoming year [4]

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 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 ★ 上次VLA模型+真机部署的圆桌受到了行业的一致好评。最近平台的同学也一直在整理对话的文稿，今天就为大家分享下第一部分" VLA的架构和模型 "相关内 容。 张强老师： 好，感谢主持人介绍，大家好，我是张强。我来自北京人形机器人中心，主要研究方向和研究背景都是在做人形机器人，大概从2021年开始做人形机器人。先后在 Fourier、GR-1 和 Embodied机器人，包括我们现在的天工平台上做了一些研究。我主要做的研究方向是运动控制，VLA 和一些基于人形机器人的世界模型和具身智 能大模型，希望大家关注我们的工作，然后今天也很高兴跟各位嘉宾。很高兴接受具身智能之心的邀请，很高兴跟各位嘉宾在一起讨论一下相关的问题，谢谢！ 完整内容欢迎加入我们的具身社区获取： 具身智能之心知识星球 主持人： 好，那我们就正式开始，那么欢迎大家来到具身智能之心的圆 ...

Core Viewpoint - The article discusses the advancements in the π series within the VLA (Vision-Language-Action) field, highlighting its role in transforming robotic learning paradigms and industry applications through continuous technological breakthroughs [2]. Group 1: Technological Advancements - The π0 model introduces Flow Matching for continuous action trajectory prediction, overcoming traditional discrete action precision limitations, providing a foundation for millimeter-level operations in precision manufacturing and autonomous driving scenarios [3]. - The π0.5 model features heterogeneous task collaborative training and hierarchical reasoning, achieving a 94% success rate in generalizing complex tasks in unfamiliar environments, while reducing data costs by 90% through human video training [3]. - The π0.6 model employs RECAP reinforcement learning for zero-shot generalization and efficient fine-tuning, surpassing human efficiency and precision in real-world applications, facilitating flexible production [3]. Group 2: Industry Impact - The π series models serve as a core reference for numerous VLA models in the industry since 2025, enabling the transition of general robots from laboratory settings to real-world applications in industrial manufacturing and home services [3]. - Companies are building their own demo machines based on the π series, such as for tasks like folding clothes and unpacking, indicating the practical implications of the technology [3]. Group 3: Learning and Development Challenges - Many beginners face difficulties in optimizing data and training VLA models based on the π series, with some spending up to six months without achieving satisfactory results [5]. - The article emphasizes the need for guided learning to help individuals gain practical experience and project work for job applications [6][11]. Group 4: Educational Offerings - The company offers a comprehensive course that covers hardware, data collection, VLA algorithms, evaluation, simulation, deployment of mainstream VLA models, and various real machine experiments [13][14]. - Participants in the course will receive a SO-100 robotic arm, enhancing hands-on learning opportunities [16]. Group 5: Target Audience - The course is aimed at individuals seeking practical experience in the VLA field, including students and professionals transitioning from traditional computer vision, robotics, or autonomous driving sectors [24].