Core Insights - The article discusses the significant technological advancements in autonomous driving over the past decade, highlighting key innovations such as Visual Transformers, BEV perception, multi-sensor fusion, end-to-end autonomous driving, large models, VLA, and world models [3][4]. Group 1: Technological Milestones - The past ten years have seen remarkable technological developments in autonomous driving, with various solutions emerging through the collision and fusion of different technologies [3]. - A roundtable discussion is set to reflect on the technological milestones in the industry, focusing on the debate between world models and VLA [4][13]. Group 2: Industry Perspectives - The roundtable will feature insights from top industry leaders, discussing the evolution of autonomous driving technology and providing career advice for newcomers in the field [4][5]. - The discussion will also cover the perspectives of academia and industry regarding L3 autonomous driving, emphasizing the convergence of research directions and the practical implementation in engineering [13]. Group 3: Future Directions - The article raises questions about the future direction of autonomous driving technology, particularly the role of end-to-end systems as a foundational element of intelligent driving technology [13]. - It highlights the ongoing competition between academic research and engineering practices in the field, suggesting a need for new entrants to adapt and innovate [13].

Group 1 - The establishment of a technical exchange group focused on autonomous driving technology has been announced, covering areas such as world models, end-to-end systems, and VLA [1] - The company invites interested individuals to join the discussion by adding a designated assistant on WeChat with specific instructions for group entry [1]

Core Viewpoint - 2023 marks the year of end-to-end production, with 2024 expected to be a significant year for end-to-end production in the automotive industry, as leading new forces and manufacturers have already achieved end-to-end production [1][3]. Group 1: End-to-End Production Development - The automotive industry is witnessing rapid development in end-to-end production, particularly in one-stage and two-stage paradigms, with one-stage methods like UniAD being prominent [1][3]. - Various one-stage methods have emerged, including perception-based, world model-based, diffusion model-based, and VLA-based approaches, indicating a strong push from both autonomous driving companies and vehicle manufacturers towards self-research and mass production of end-to-end autonomous driving [3][5]. Group 2: Course Overview - A course titled "End-to-End and VLA Autonomous Driving" has been launched, focusing on cutting-edge algorithms in both one-stage and two-stage end-to-end methods, aimed at bridging academic and industrial advancements [5][15]. - The course is structured into several chapters, covering topics such as the history and evolution of end-to-end algorithms, background knowledge on VLA, and detailed discussions on two-stage and one-stage end-to-end methods [9][10][12]. Group 3: Key Technologies and Techniques - The course emphasizes key technologies such as BEV perception, visual language models (VLM), diffusion models, and reinforcement learning, which are essential for mastering the latest advancements in autonomous driving [5][11]. - The second chapter of the course is highlighted as crucial for understanding the most frequently asked technical keywords in job interviews over the next two years [10]. Group 4: Practical Applications and Outcomes - The course includes practical assignments, such as RLHF fine-tuning, allowing participants to apply their knowledge in real-world scenarios and understand how to build and experiment with reinforcement learning modules [13][19]. - By completing the course, participants are expected to reach a level equivalent to one year of experience as an end-to-end autonomous driving algorithm engineer, gaining a comprehensive understanding of various methodologies and their applications [19].

Core Viewpoint - The article discusses the evolution and current state of end-to-end algorithms in autonomous driving, highlighting the emergence of various subfields, particularly those based on Visual Language Models (VLA) and the increasing interest in these technologies within both academia and industry [1][3]. Summary by Sections End-to-End Algorithms - End-to-end algorithms are central to the current mass production of autonomous driving technologies, involving a rich technology stack. There are primarily two paradigms: single-stage and two-stage. The single-stage approach, exemplified by UniAD, directly models vehicle trajectories from sensor inputs, while the two-stage approach outputs trajectories based on perception results [1]. VLA and Related Technologies - The development has progressed from modular production algorithms to end-to-end systems and now to VLA. Key technologies involved include BEV perception, Visual Language Models (VLM), diffusion models, reinforcement learning, and world models. The article emphasizes the importance of understanding these technologies to grasp the cutting-edge directions in both academia and industry [3]. Courses Offered - The article promotes two courses aimed at helping individuals quickly and efficiently learn about end-to-end and VLA in autonomous driving. The courses are designed for those new to large models and VLA, covering foundational theories and practical applications [3][10]. Course Content - The "VLA and Large Model Practical Course" focuses on VLA, starting from VLM as an interpreter for autonomous driving, and covers modular and integrated VLA, as well as mainstream inference-enhanced VLA. It includes detailed theoretical foundations and practical assignments to build VLA models and datasets from scratch [3][10]. Instructor Team - The courses are led by experienced instructors from both academia and industry, with backgrounds in multi-modal perception, autonomous driving VLA, and large model frameworks. They have published numerous papers in top conferences and have substantial practical experience in the field [7][9][10]. Target Audience - The courses are aimed at individuals with a foundational understanding of autonomous driving, familiar with basic modules, and possessing knowledge of transformer models, reinforcement learning, and BEV perception. A background in probability theory, linear algebra, and programming in Python and PyTorch is also recommended [13].

Core Insights - The article discusses two significant advancements in the field of embodied intelligence and VLA (Vision-Language Action) models, highlighting their potential to overcome existing challenges in the domain [3][7]. Group 1: VLA-Adapter - VLA-Adapter aims to improve the direct mapping from VLM (Vision-Language Model) features to action space without heavily relying on robotic data. The research team found that increasing the parameter count and introducing pre-trained robotic data did not significantly enhance model performance on general benchmarks [3]. - The new mapping scheme proposed by the team allows the model to achieve superior performance even at a 0.5 billion parameter scale, reducing training costs and lowering the entry barrier for VLA models [3]. Group 2: TrajBooster - TrajBooster is the first full-body humanoid operation VLA solution that addresses data scarcity issues for training VLA models in bipedal humanoid tasks. The scarcity arises from the high cost of remote operation data and the challenges of using existing heterogeneous robot data for training [7]. - By focusing on trajectory-centered methods, TrajBooster efficiently utilizes cross-body data, achieving full-body operation in bipedal robots with just 10 minutes of real machine remote operation data for fine-tuning [7]. Group 3: Contributors - Wang Yihao, a fourth-year PhD student at Beijing University of Posts and Telecommunications, is involved in the VLA-Adapter project and has contributed significantly to the field of embodied intelligence and VLA models [13]. - Liu Jiacheng, a second-year PhD student at Zhejiang University and West Lake University, leads the TrajBooster project, which is the only fully open-source work covering humanoid data collection, cross-body data enhancement, VLA model training, and hardware deployment [13].

Core Insights - The article discusses the current state and future prospects of autonomous driving technology, emphasizing the importance of AI and various modeling approaches in achieving higher levels of automation [4][6][9]. Group 1: Industry Development - The autonomous driving industry is rapidly evolving, with significant advancements expected in the next few years, particularly in AI and related fields [4]. - Companies like Waymo and Tesla are leading the way in achieving Level 4 (L4) automation, while Level 5 (L5) may take at least five more years to realize [4][6]. - The integration of Vision-Language Models (VLA) is seen as a key to enhancing decision-making capabilities in autonomous vehicles, addressing long-tail problems that pure end-to-end models may struggle with [6][9]. Group 2: Technical Approaches - The article outlines different modeling approaches in autonomous driving, including end-to-end models and the emerging VLA paradigm, which combines language processing with visual data to improve reasoning and decision-making [5][9]. - The effectiveness of current autonomous driving systems is still limited, with many challenges remaining in achieving full compliance with traffic regulations and safety standards [10][14]. - The discussion highlights the importance of data and cloud computing capabilities in narrowing the performance gap between domestic companies and leaders like Tesla [14][15]. Group 3: Talent and Education - There is a recognized talent gap in the autonomous driving sector, with a strong recommendation for students to pursue AI and computer science to prepare for future opportunities in the industry [4][6]. - The article suggests that practical experience in larger autonomous driving companies may provide better training and growth opportunities compared to smaller robotics firms [16][20].

Core Viewpoint - The company is seeking collaboration with global practitioners in the embodied intelligence field to enhance capabilities in various areas such as technical services, training, course development, and research guidance [1]. Group 1: Collaboration Opportunities - There is an increasing demand from partners and small companies for the company to empower them through solutions, data collection, technology upgrades, and corporate training [1]. - The company is inviting outstanding partners to join in driving significant industry progress [1]. Group 2: Compensation and Resources - The company will offer high compensation and abundant industry resources to collaborators [2]. Group 3: Focus Areas - Key focus areas for collaboration include but are not limited to: VLA, VLN, Diffusion Policy, Reinforcement Learning, VLA+RL, remote operation, motion capture, sim2real, multimodal large models, simulation, motion control, end-to-end systems, and 3D perception [3]. Group 4: Job Description - The positions are primarily aimed at embodied course development, solution research and development, hardware development, and training collaboration, targeting both B-end (enterprises, universities, research institutes) and C-end (students, job seekers) [4]. Group 5: Contact Information - Interested parties can add WeChat oooops-life for further inquiries [5].

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]

最近有同学后台留言，刚开学导师跨行做具身，让自己先去摸索下，最好能产出论文和项目。没有基础最快能 多久出论文？ 针对跨行或者新入门的同学，我们一直建议先把基础打好。然后找一些研究价值比较大的领域突破。特别是有 一定的工作基础、数据基础的领域，如果完全不成熟，没有人同行后期科研的难度很大。 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。如果有同学不知道怎么选择方向，可以多关注这个领域！具身智能之心最 近也出品了一套1v6的科研辅导论文课程，也欢迎关注报名。 那么VLA是什么？ 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 ...

9月24日晚，地瓜机器人与锦秋基金联手邀请来30 余位 「机器人头号玩家」 ，在杭州举办了一场机器人精酿Party。 来自 地瓜机器人 生态负责人胡春旭、云平台负责人秦玉森、算法负责人隋伟、锦秋基金合伙人臧天宇、锦秋基金投资副总裁Cindy、阿里云生态负责人 陈博 、 X-Man科沃斯蒲公英加速器总经理赵文景 空降现场，一起和科技大厂产品达人、技术专家、创业先锋们微醺开聊 "机器人的新一代故事" 。 现场机器人玩家们硬核开麦， 开发者们灵感捧杯 到我的客 杯精酿互诉机器 会 门对小对物 # # 地瓜机器人 醫 锦秋基金 ir ans and 12 12 statis 杯精酿互诉机器人信 杯里有精酿,哪里有 H El B 精蛋TE 地瓜机器人 鲨 锦桃基金 杯精酿互诉机器人信仰 I 力校准液制作中 # # 地瓜机器人 器 锦秋基金 同时，锦秋基金就现场大家对 VLA 不同观点的讨论，做了以下记录 挑战派 两条腿走路：上层大模型负责理解/任务分解，底层RL/规控负责约束满足与实时稳定；协同进化。 自主数据生成与仿真增强：用RL+物理仿真（动力学/碰撞/库伦摩擦）造数据、学策略，提高泛化；像"孩子学走路"靠自我试错 ...