以下文章来源于星河频率 ，作者毛心如 星河频率 . 来 源 丨 星河频率 （ID：robo—wave） 作者丨 毛心如 图源丨 midjourney 蓄力助跑，仅凭一次尝试，星动纪元 L7 就以 95 .64 1cm 的成绩， 创下人形机器人跳高世界纪录 。 171cm 的身高，65kg 的体重，即便是普通人也未必能蹦出来这么高、这么标准的超级玛丽跳。 尽管本届世界人形机器人运动会上不乏各类「翻车」名场面，吸引了不少眼球，不可忽视的是，无论是跑步、跳高还是跳远，这些项目都深度考验了 机器 人「算法+硬件」 高度耦合 的能力。 与此同时，在本届运动会中夺冠次数最多的宇树科技，其创始人王兴兴在世界机器人大会论坛上的发言，却因对当前热门的 VLA 路线提出质疑，而被不少 人称为「炸裂发言」甚至「暴论」。 关注通用机器人的一切。 同样作为冠军团队的星动纪元，其创始人陈建宇却对 VLA 表达出与王兴兴不同的态度。 观点分野的背后，实则是两家公司对 「如何让机器人变得更强大」 这一目标，所采取的不同实践路径—— 一条是「硬件先行」，另一条是「软硬一体、垂 直整合」 。 垂直整合和 硬件先行的观念分野 两位创始人的背景差异 ...

理想VLA司机大模型已经上车了！从发布会上看，VLA 能力的提升集中体现在三点：更懂语义 （多模态输入）、更擅长推理（思维链）、更接近人类驾驶直觉（轨迹规划）。发布会上展示了 四个核心能力：空间理解能力、思维能力、沟通与记忆能力以及行为能力。 ⼀、VLA科研论文辅导课题来啦⭐ 其中思维能力、沟通与记忆能力是语言模型赋予的能力，其中记忆能力还用到了RAG。下面是理 想VLA司机大模型思维链输出的demo：结合了动态目标、静态元素、导航地图、空间理解等等元 素。毫无疑问，VLA已经是自动驾驶学术界和工业界最为关注的方向。 而VLA是从VLM+E2E一路发展过来的，涵盖了端到端、轨迹预测、视觉语言模型、强化学习等多 个前沿技术栈。。而传统的BEV感知、车道线、Occupancy等工作相对较少出现在顶会了，最近也 有很多同学陆续来咨询柱哥，传统的感知、规划这块还能继续发论文吗？感觉工作都已经被做的 七七八八了，审稿人会打高分吗？ 说到传统的感知、规划等任务，工业界都还在继续优化方案！但学术界基本都慢慢转向大模型与 VLA了，这个领域还有很多工作可以做的子领域... 之前我们已经开展了第一期VLA论文指导班，反响很不错 ...

Core Viewpoint - The article discusses the advancements in the VLA (Vision-Language Action) driver model by Li Auto, highlighting its four core capabilities: spatial understanding, reasoning, communication and memory, and behavioral capabilities. It emphasizes the significance of VLA in the field of autonomous driving, indicating a shift in focus from traditional perception and planning tasks to large models and VLA technologies [2][4]. Summary by Sections VLA Model Capabilities - The VLA model integrates dynamic targets, static elements, navigation maps, and spatial understanding, showcasing a more human-like reasoning ability. This positions VLA as a leading focus in both academia and industry for autonomous driving [2]. Shift in Research Focus - Traditional perception and planning tasks are becoming less prominent in top conferences, with academia increasingly shifting towards large models and VLA. Despite this, the industry continues to optimize traditional methods, indicating ongoing opportunities in both areas [4]. Educational Program - An educational program is introduced to help students systematically grasp key theoretical knowledge in VLA, enhance practical coding skills, and develop their own research ideas. The program includes a structured 12-week online group research course followed by 2 weeks of paper guidance and a 10-week maintenance period [5][34]. Course Structure - The course spans 14 weeks, covering topics from introductory lessons to advanced VLA models and paper writing methodologies. Each week focuses on different aspects of VLA and autonomous driving, culminating in a final project report and submission guidance [8][10][35]. Target Audience - The program is designed for master's and doctoral students in VLA and autonomous driving, individuals seeking to enhance their resumes for further studies abroad, and professionals in the AI and autonomous driving sectors looking to deepen their algorithmic knowledge [14][24]. Course Requirements - Participants are expected to have a foundational understanding of deep learning, basic programming skills in Python, and familiarity with PyTorch. Access to high-performance computing resources is recommended for optimal learning [20][21]. Course Highlights - The program features a "2+1" teaching model with experienced instructors, ensuring comprehensive support throughout the learning process. It emphasizes academic integrity and provides a structured evaluation system to enhance the learning experience [22][23].

Core Viewpoint - The article discusses the development and significance of end-to-end (E2E) algorithms in autonomous driving, emphasizing the integration of various advanced technologies such as large language models (LLMs), diffusion models, and reinforcement learning (RL) in enhancing the capabilities of autonomous systems [21][31]. Summary by Sections Section 1: Overview of End-to-End Autonomous Driving - The first chapter provides a comprehensive overview of the evolution of end-to-end algorithms, explaining the transition from modular approaches to end-to-end solutions, and discussing the advantages and challenges of different paradigms [40]. Section 2: Background Knowledge - The second chapter focuses on the technical stack associated with end-to-end systems, detailing the importance of LLMs, diffusion models, and reinforcement learning, which are crucial for understanding the future job market in this field [41][42]. Section 3: Two-Stage End-to-End Systems - The third chapter delves into two-stage end-to-end systems, exploring their emergence, advantages, and disadvantages, while also reviewing notable works in the field such as PLUTO and CarPlanner [42][43]. Section 4: One-Stage End-to-End and VLA - The fourth chapter highlights one-stage end-to-end systems, discussing various subfields including perception-based methods and the latest advancements in VLA (Vision-Language Alignment), which are pivotal for achieving the ultimate goals of autonomous driving [44][50]. Section 5: Practical Application and RLHF Fine-Tuning - The fifth chapter includes a major project focused on RLHF (Reinforcement Learning from Human Feedback) fine-tuning, providing practical insights into building pre-training and reinforcement learning modules, which are applicable to VLA-related algorithms [52]. Course Structure and Learning Outcomes - The course aims to equip participants with a solid understanding of end-to-end autonomous driving technologies, covering essential frameworks and methodologies, and preparing them for roles in the industry [56][57].

Core Viewpoint - The article discusses recent advancements in reinforcement learning (RL) and its applications in robotics, particularly focusing on the VLA (Vision-Language Action) models and diffusion policies, highlighting their potential to handle complex tasks that traditional RL struggles with [2][4][35]. Method Paradigms - Traditional RL and imitation learning combined with Sim2Real techniques are foundational approaches in robotics [3]. - VLA models differ fundamentally from traditional RL by using training data distributions to describe task processes and goals, allowing for the execution of more complex tasks [4][35]. - Diffusion Policy is a novel approach that utilizes diffusion models to generate continuous action sequences, demonstrating superior capabilities in complex task execution compared to traditional RL methods [4][5]. Application Scenarios - The article categorizes applications into two main types: basic motion control for humanoid and quadruped robots, and complex/long-range operational tasks [22][23]. - Basic motion control primarily relies on RL and Sim2Real, with current implementations still facing challenges in achieving fluid motion akin to human or animal movements [22]. - For complex tasks, architectures typically involve a pre-trained Vision Transformer (ViT) encoder and a large language model (LLM), utilizing diffusion or flow matching for action output [23][25]. Challenges and Future Directions - The article identifies key challenges in the field, including the need for better simulation environments, effective domain randomization, and the integration of external goal conditions [35]. - It emphasizes the importance of human intention in task definition and the limitations of current models in learning complex tasks without extensive human demonstration data [35][40]. - Future advancements may involve multi-modal input predictions for task goals and the potential integration of brain-machine interfaces to enhance human-robot interaction [35].

Core Viewpoint - The article emphasizes the convergence of autonomous driving technology, indicating a shift from numerous diverse approaches to a more unified model, which raises the technical barriers in the industry [1] Group 1 - The industry is witnessing a trend where previously many directions requiring algorithm engineers are now consolidating into unified models such as one model, VLM, and VLA [1] - The article encourages the establishment of a large community to support individuals in the industry, highlighting the limitations of individual efforts [1] - A new job and industry-related community is being launched to facilitate discussions on industry trends, company developments, product research, and job opportunities [1]

Core Viewpoint - The article discusses the advancements of the Li Auto VLA driver model, highlighting its enhanced capabilities in understanding semantics, reasoning, and trajectory planning, which are crucial for autonomous driving [1][3]. Summary by Sections VLA Model Capabilities - The VLA model has improved in three main areas: better semantic understanding through multimodal input, enhanced reasoning abilities via thinking chains, and closer alignment with human driving intuition through trajectory planning [1]. - Four core capabilities of the VLA model are showcased: spatial understanding, reasoning, communication and memory, and behavioral capabilities [1][3]. Development and Research Trends - The VLA model has evolved from VLM+E2E, incorporating various cutting-edge technologies such as end-to-end learning, trajectory prediction, visual language models, and reinforcement learning [5]. - While traditional perception and planning tasks are still being optimized in the industry, the academic community is increasingly shifting focus towards large models and VLA, indicating a wealth of subfields still open for research [5]. VLA Research Guidance Program - A VLA research paper guidance program has been initiated, receiving positive feedback, with many students eager for a second session. The program aims to help participants systematically grasp key theoretical knowledge and develop their own research ideas [6]. - The program includes a structured curriculum over 14 weeks, covering topics from traditional end-to-end autonomous driving to writing methodologies for research papers [9][11]. Enrollment and Course Structure - The program is limited to 6-8 participants per session, targeting students at various academic levels interested in VLA and autonomous driving [12]. - Participants will gain insights into classic and cutting-edge papers, coding implementations, and methods for selecting research topics and writing papers [13][14]. Course Highlights - The course emphasizes a comprehensive learning experience with a "2+1" teaching model, involving main instructors and experienced research assistants to support students throughout the program [22]. - Students will receive guidance on coding, research ideas, and writing methodologies, culminating in the production of a research paper draft [31][32]. Required Skills and Resources - Participants are expected to have a foundational understanding of deep learning, basic programming skills in Python, and familiarity with PyTorch [19]. - The program encourages the use of high-performance computing resources, ideally with multiple GPUs, to facilitate research and experimentation [19]. Conclusion - The VLA model represents a significant advancement in autonomous driving technology, with ongoing research and educational initiatives aimed at fostering innovation in this field [1][5][31].

Core Insights - The article discusses the recent advancements in the Li Auto VLA driver model, highlighting its improved capabilities in understanding semantics, reasoning, and trajectory planning, which are crucial for autonomous driving [1][3]. Group 1: VLA Model Capabilities - The VLA model's enhancements focus on four core abilities: spatial understanding, reasoning, communication and memory, and behavioral capabilities [1]. - The reasoning and communication abilities are derived from language models, with memory capabilities utilizing RAG [3]. Group 2: Research and Development Trends - The VLA model has evolved from VLM+E2E, incorporating various cutting-edge technologies such as end-to-end learning, trajectory prediction, visual language models, and reinforcement learning [5]. - While traditional perception and planning tasks are still being optimized in the industry, the academic community is increasingly shifting towards large models and VLA, indicating a wealth of subfields still open for research [5]. Group 3: VLA Research Guidance Program - A VLA research paper guidance program has been initiated, aimed at helping participants systematically grasp key theoretical knowledge and develop their own research ideas [6]. - The program includes a structured 12-week online group research course followed by 2 weeks of paper guidance and a 10-week maintenance period for paper development [14][34]. Group 4: Course Structure and Content - The course covers various topics over 14 weeks, including traditional end-to-end autonomous driving, VLA end-to-end models, and writing methodologies for research papers [9][11][35]. - Participants will gain insights into classic and cutting-edge papers, coding skills, and methods for writing and submitting research papers [20][34]. Group 5: Enrollment and Requirements - The program is limited to 6-8 participants per session, targeting individuals with a background in deep learning and basic knowledge of autonomous driving algorithms [12][15]. - Participants are expected to have a foundational understanding of Python and PyTorch, with access to high-performance computing resources recommended [21].

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Core Insights - The article discusses the advancements of the Li Auto VLA driver model, highlighting its improved capabilities in understanding semantics, reasoning, and trajectory planning, which are crucial for autonomous driving [1][3][5] Group 1: VLA Model Capabilities - The VLA model demonstrates enhanced semantic understanding through multimodal input, improved reasoning via thinking chains, and a closer approximation to human driving intuition through trajectory planning [1] - Four core abilities of the VLA model are showcased: spatial understanding, reasoning ability, communication and memory capability, and behavioral ability [1][3] Group 2: Research and Development Trends - The VLA model has evolved from VLM+E2E, integrating various cutting-edge technologies such as end-to-end learning, trajectory prediction, visual language models, and reinforcement learning [5] - While traditional perception and planning tasks are still being optimized in the industry, the academic community is increasingly shifting focus towards large models and VLA, indicating a wealth of subfields still open for exploration [5] Group 3: VLA Research Guidance Program - A second session of the VLA research paper guidance program is being launched, aimed at helping participants systematically grasp key theoretical knowledge and develop their own research ideas [6][31] - The program includes a structured curriculum over 12 weeks of online group research, followed by 2 weeks of paper guidance and a 10-week maintenance period for paper development [14][31] Group 4: Course Structure and Requirements - The course is designed for a maximum of 8 participants, focusing on those pursuing master's or doctoral degrees in VLA and autonomous driving, as well as professionals in the AI field seeking to enhance their algorithmic knowledge [12][13] - Participants are expected to have a foundational understanding of deep learning, basic programming skills in Python, and familiarity with PyTorch [19][20] Group 5: Course Outcomes - Participants will gain insights into classic and cutting-edge papers, coding implementations, and methodologies for selecting research topics, conducting experiments, and writing papers [14][31] - The program aims to produce a draft of a research paper, enhancing participants' academic profiles for further studies or employment opportunities [14][31]