Group 1 - The article announces the recruitment of 10 outstanding partners for the autonomous driving sector, focusing on course development, paper guidance, and hardware research [2] - The main areas of expertise sought include large models, multimodal models, diffusion models, end-to-end systems, embodied interaction, joint prediction, SLAM, 3D object detection, world models, closed-loop simulation, and model deployment and quantization [3] - Candidates are preferred from universities ranked within the QS200, holding a master's degree or higher, with priority given to those with significant conference contributions [4] Group 2 - The compensation package includes resource sharing for job seeking, doctoral studies, and overseas study recommendations, along with substantial cash incentives and opportunities for entrepreneurial project collaboration [5] - Interested parties are encouraged to add WeChat for consultation, specifying "organization/company + autonomous driving cooperation inquiry" [6]

Core Insights - The article emphasizes the importance of building a solid foundation in research before diving into complex topics like VLA (Vision-Language-Action) in embodied intelligence [1][6] - VLA is highlighted as a significant research area that breaks traditional single-task limitations, allowing robots to make autonomous decisions in diverse environments [4][6] - The article discusses the rapid development of the embodied intelligence sector, with various teams transitioning from laboratory research to commercialization, supported by major tech companies [6] Summary by Sections VLA Overview - VLA enables the execution of commands through language, facilitating continuous actions and enhancing user experience [2] - It represents a shift from traditional methods, allowing for multi-tasking capabilities in robots across various applications [4] Industry Development - The embodied intelligence field is experiencing robust growth, with companies like Unitree and tech giants like Huawei and Tencent actively investing in this area [6] - The collaboration between academia and industry is being fostered through various projects and research initiatives [4][6] Educational Initiatives - A specialized course on VLA research is being offered to help students navigate the complexities of the field, focusing on practical skills and research methodologies [10][12] - The course aims to equip students with the ability to identify research opportunities, design experiments, and write academic papers [12][16] Learning Outcomes - Students completing the course will gain comprehensive knowledge of VLA models, experimental design, and the academic writing process [16] - The program emphasizes the development of independent research capabilities and the ability to produce a complete research paper [16]

Core Insights - The article discusses the evolving landscape of robotics, highlighting the importance of collaboration among industry players and the need for innovative solutions in the field [2][14]. Group 1: Industry Challenges - There is a lack of foundational data in robotics compared to other fields like the internet and autonomous driving, which hampers the development of embodied interaction platforms [18]. - Current training methods for VLA (Vision-Language Agents) rely heavily on superficial data, lacking essential physical constraints such as dynamics and collision, leading to instability in practical applications [18]. - The engineering challenges persist, with the need for parameter tuning in both dynamic models and reward systems, resulting in lengthy and costly training-validation cycles [18]. Group 2: Development Strategies - Short-term implementation of VLA is hindered by the absence of time and constraint concepts in the "brain" outputs, necessitating a clean-up and constraint layer for planning and control [18]. - A rule-based safety net is recommended for controlled environments, combining rules with learnable algorithms for optimization, allowing for initial commercial delivery while gradually building data loops and capabilities [18]. - The advancement of VLA requires addressing two key factors: the shortage of talent in foundational model development and the lack of entities capable of commercializing these models [18]. Group 3: Future Directions - A dual approach is suggested, where upper-level large models handle understanding and task decomposition, while lower-level reinforcement learning and control ensure constraint satisfaction and real-time stability [18]. - The use of reinforcement learning combined with physical simulation is proposed to generate data and learn strategies, akin to how children learn to walk through trial and error [18]. - There is optimism for the long-term potential of learning-based control systems, which, despite being in early stages, possess the ability to generalize and adapt effectively [18].

Core Viewpoint - 2023 is identified as the year of end-to-end production, with 2024 expected to be a significant year for this development in the automotive industry, particularly in autonomous driving technology [1][3]. Group 1: End-to-End Production - Leading new forces and manufacturers have already achieved end-to-end production [1]. - There are two main paradigms in the industry: one-stage and two-stage approaches, with UniAD being a representative of the one-stage method [1]. Group 2: Development Trends - Since last year, the one-stage end-to-end approach has rapidly evolved, leading to various derivatives such as perception-based, world model-based, diffusion model-based, and VLA-based one-stage methods [3]. - Major autonomous driving companies are focusing on self-research and mass production of end-to-end autonomous driving solutions [3]. Group 3: Course Offerings - A course titled "End-to-End and VLA Autonomous Driving" has been launched, covering cutting-edge algorithms in both one-stage and two-stage end-to-end approaches [5]. - The course aims to provide insights into the latest technologies in the field, including BEV perception, visual language models, diffusion models, and reinforcement learning [5]. Group 4: Course Structure - The course consists of several chapters, starting with an introduction to end-to-end algorithms, followed by background knowledge essential for understanding the technology stack [9][10]. - The second chapter focuses on the most frequently asked technical keywords in job interviews over the next two years [10]. - Subsequent chapters delve into two-stage end-to-end methods, one-stage end-to-end methods, and practical assignments involving RLHF fine-tuning [12][13]. Group 5: Learning Outcomes - Upon completion, participants are expected to reach a level equivalent to one year of experience as an end-to-end autonomous driving algorithm engineer [19]. - The course aims to deepen understanding of key technologies such as BEV perception, multimodal large models, and reinforcement learning, enabling participants to apply learned concepts to real projects [19].

Core Viewpoint - The field of embodied intelligence is experiencing unprecedented attention, yet key issues remain unresolved, including data scarcity and differing technical approaches [1][2][3] Group 1: Data and Technical Approaches - The industry is divided into two factions: the "real machine" faction, which relies on real-world data collection, and the "synthetic" faction, which believes in the feasibility of synthetic data for model training [5][12] - Galaxy General, representing the synthetic faction, argues that achieving generalization in embodied intelligence models requires trillions of data points, which is unsustainable through real-world data alone [8][9] - The "real machine" faction challenges the notion that real-world data is prohibitively expensive, suggesting that with sufficient investment, data collection can be scaled effectively [12][14] Group 2: Model Architecture - Discussions around the architecture of embodied intelligence models highlight a divide between end-to-end and layered approaches, with some experts advocating for a unified model while others support a hierarchical structure [15][19] - The layered architecture is seen as more aligned with biological evolution, while the end-to-end approach is criticized for potential error amplification [19][20] - The debate extends to the relevance of VLA (Vision-Language Alignment) versus world models, with some experts arguing that VLA is currently more promising due to its data efficiency [21][22] Group 3: Industry Trends and Infrastructure - The scaling law in embodied intelligence is beginning to emerge, indicating that expanding model and data scales could be effective [24] - The industry is witnessing an acceleration in the deployment of embodied intelligence technologies, with various companies sharing their experiences in human-robot interaction and industrial applications [24][29] - Cloud service providers, particularly Alibaba Cloud, are emphasized as crucial players in supporting the infrastructure needs of embodied intelligence companies, especially as they transition to mass production [29][31] Group 4: Alibaba Cloud's Role - Alibaba Cloud has been preparing for the exponential growth in data and computational needs associated with embodied intelligence, having developed capabilities to handle large-scale data processing and model training [33][35] - The company offers a comprehensive suite of cloud-based solutions to support both real and synthetic data production, enhancing efficiency and reducing costs [35][36] - Alibaba Cloud's unique position as a model provider and its engineering capabilities are seen as significant advantages in the rapidly evolving embodied intelligence landscape [37][41]

Core Insights - The article emphasizes the importance of building a solid foundation in research before diving into complex topics like VLA (Vision-Language-Action) in embodied intelligence [1][6] - VLA is highlighted as a transformative model that allows robots to perform tasks based on language instructions, breaking the limitations of traditional single-task training [4][7] - The article discusses the rapid development of the embodied intelligence sector, with various teams transitioning from research to commercialization, and major tech companies actively investing in this field [6] Summary by Sections VLA Overview - VLA enables robots to autonomously make decisions in diverse environments, significantly enhancing their adaptability and application across industries such as manufacturing and logistics [4][6] - The model has become a research hotspot, fostering collaboration between academia and industry through various projects like pi0, RT-2, and OpenVLA [4][7] Industry Development - The embodied intelligence field is experiencing robust growth, with companies like Unitree, Zhiyuan, and major tech players like Huawei and Tencent making significant strides [6] - There is a growing interest in VLA-related research, with many seeking guidance to quickly enter or transition within this domain [6] Course Offerings - A specialized course on VLA research is introduced, focusing on the theoretical and practical aspects of embodied intelligence, including simulation environment setup and experimental design [10][12] - The course aims to cultivate independent research capabilities, guiding students from idea generation to the completion of a research paper [12][17] Learning Outcomes - Participants will gain comprehensive knowledge of VLA models, practical experience in simulation, and skills in academic writing and research methodology [17] - The course is designed to help students identify research opportunities and navigate the complexities of the embodied intelligence landscape [12][16]

想象一下，如果能通过语言下达指令，并且丝滑执行任何你想要的动作，是一件多么幸福的事情！如果能长时 间连续动作完成，将会非常方便。下面给大家介绍下VLA到底是啥？ VLA打破了传统方法的单任务局限，使得机器人能够在多样化的场景中自主决策，灵活应对未见过的环境， 广泛应用于制造业、物流和家庭服务等领域。此外，VLA模型已成为研究热点，推动了多个前沿项目的发 展，如pi0、RT-2、OpenVLA、QUAR-VLA和HumanVLA，这些研究促进了学术界与工业界的合作。其适应性 体现在能够应用于机械臂、四足机器人和人形机器人等多种平台，为各类智能机器人的发展提供了广泛的潜力 和实际应用价值，成为智能机器人领域的关键驱动力。 从今年各个机器人与AI顶会来看，VLA及其相关衍生方向，占据了近一半的具身产出。特别是长程操作、泛 化、少样本、VLA+RL、人形相关。 从产业角度看，国内外具身智能领域正处于蓬勃发展阶段，Unitree、智元、星海图、银河通用、逐际动力等团 队从实验室走向商业化，华为、京东、腾讯等科技巨头也积极布局，与国外Tesla、Figure AI等公司正在一起 推动这一领域的发展。 很多同学后台留言，咨 ...

从今年各个机器人与AI顶会来看，VLA及其相关衍生方向，占据了近一半的具身产出。特别是长程操作、 泛化、少样本、VLA+RL、人形相关。 想象一下，如果能通过语言下达指令，并且丝滑执行任何你想要的动作，是一件多么幸福的事情！如果能 长时间连续动作完成，将会非常方便。下面给大家介绍下VLA到底是啥？ VLA打破了传统方法的单任务局限，使得机器人能够在多样化的场景中自主决策，灵活应对未见过的环 境，广泛应用于制造业、物流和家庭服务等领域。此外，VLA模型已成为研究热点，推动了多个前沿项目 的发展，如pi0、RT-2、OpenVLA、QUAR-VLA和HumanVLA，这些研究促进了学术界与工业界的合作。 其适应性体现在能够应用于机械臂、四足机器人和人形机器人等多种平台，为各类智能机器人的发展提供 了广泛的潜力和实际应用价值，成为智能机器人领域的关键驱动力。 从产业角度看，国内外具身智能领域正处于蓬勃发展阶段，Unitree、智元、星海图、银河通用、逐际动力 等团队从实验室走向商业化，华为、京东、腾讯等科技巨头也积极布局，与国外Tesla、Figure AI等公司正 在一起推动这一领域的发展。 很多同学后台留言，咨 ...

Group 1 - The article announces the recruitment of 10 partners for the autonomous driving sector, focusing on course development, paper guidance, and hardware research [2] - The recruitment targets individuals with expertise in various advanced technologies such as large models, multimodal models, and 3D target detection [3] - Candidates from QS200 universities with a master's degree or higher are preferred, especially those with significant conference contributions [4] Group 2 - The compensation package includes resource sharing for job seeking, PhD recommendations, and study abroad opportunities, along with substantial cash incentives [5] - The company encourages potential partners to reach out via WeChat for collaboration inquiries, specifying the need to mention their organization or company [6]

Group 1 - The establishment of a technical exchange group focused on autonomous driving technologies has been announced [1] - The group aims to facilitate discussions on various topics such as world models, end-to-end systems, and VLA [1] - The initiative coincides with the back-to-school season and autumn recruitment period, indicating a strategic timing for engagement [1]