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]

Core Viewpoint - The article emphasizes the significance of VLA (Vision-Language Alignment) as a new milestone in the mass production of autonomous driving technology, highlighting the progressive development from E2E (End-to-End) to VLA, and the growing interest from professionals in transitioning to this field [1][11]. Course Overview - The course titled "End-to-End and VLA Autonomous Driving Small Class" aims to provide in-depth knowledge of E2E and VLA algorithms, addressing the challenges faced by individuals looking to transition into this area [1][12]. - The curriculum is designed to cover various aspects of autonomous driving technology, including foundational knowledge, advanced models, and practical applications [5][15]. Course Structure - **Chapter 1**: Introduction to End-to-End Algorithms, covering the historical development and the transition from modular to end-to-end approaches, including the advantages and challenges of each paradigm [17]. - **Chapter 2**: Background knowledge on E2E technology stacks, focusing on key areas such as VLA, diffusion models, and reinforcement learning, which are crucial for future job interviews [18]. - **Chapter 3**: Exploration of two-stage end-to-end methods, discussing notable algorithms and their advantages compared to one-stage methods [18]. - **Chapter 4**: In-depth analysis of one-stage end-to-end methods, including various subfields like perception-based and world model-based approaches, culminating in the latest VLA techniques [19]. - **Chapter 5**: Practical assignment focusing on RLHF (Reinforcement Learning from Human Feedback) fine-tuning, providing hands-on experience with pre-training and reinforcement learning modules [21]. Target Audience and Learning Outcomes - The course is aimed at individuals with a foundational understanding of autonomous driving and related technologies, such as transformer models and reinforcement learning [28]. - Upon completion, participants are expected to achieve a level equivalent to one year of experience as an end-to-end autonomous driving algorithm engineer, mastering various methodologies and being able to apply learned concepts to real-world projects [28].

Core Viewpoint - The article discusses the launch of the Li Auto i8, which is the first model equipped with the VLA driver model, highlighting its advancements in understanding semantics, reasoning, and human-like driving intuition [2][7]. Summary by Sections VLA Driver Model Capabilities - The VLA model enhances four core capabilities: spatial understanding, reasoning ability, communication and memory, and behavioral ability [2]. - It can comprehend natural language commands during driving, set specific speeds based on past memories, and navigate complex road conditions while avoiding obstacles [5]. Industry Trends and Educational Initiatives - The VLA model represents a new milestone in the mass production of autonomous driving technology, prompting many professionals from traditional fields to seek transition into VLA-related roles [7]. - The article introduces a new course titled "End-to-End and VLA Autonomous Driving," designed to help individuals transition into this field by providing in-depth knowledge and practical skills [21][22]. Course Structure and Content - The course covers various topics, including end-to-end background knowledge, large language models, BEV perception, diffusion model theory, and reinforcement learning [12][26]. - It aims to build a comprehensive understanding of the research landscape in autonomous driving, focusing on both theoretical and practical applications [22][23]. Job Market and Salary Insights - The demand for VLA/VLM algorithm experts is high, with salary ranges for positions such as VLA model quantization deployment engineers and VLM algorithm engineers varying from 40K to 120K [15]. - The course is tailored for individuals looking to enhance their skills or transition into the autonomous driving sector, emphasizing the importance of mastering multiple technical domains [19][41].

Group 1: Li Auto - Li Auto's i8 features the VLA "driver model," marking a significant advancement in intelligent driving following the previous VLM introduction [1] - The VLA model includes a newly designed spatial encoder that utilizes language models and logical reasoning to provide driving decisions, predicting trajectories of other vehicles and pedestrians through a diffusion model [1] - The inference frame rate of the VLA is approximately 10 Hz, more than tripling the previous VLM's rate of 3 Hz [1] Group 2: XPeng Motors - XPeng G7 officially commenced deliveries on July 7, with a clear timeline for the Ultra version's VLA and VLM software updates [2] - The VLA software OTA update is scheduled for September 2025, with VLM software upgrades following in November 2025, and personalized recommendations by December 2025 [2] - The XPeng G7 Ultra version is equipped with three self-developed Turing AI chips, boasting a total computing power of 2250 TOPS, positioning it as a leader among mass-produced models [2] Group 3: Chery Automobile - Chery plans to introduce the VLA and world model technology into fuel vehicles by 2025 through its Falcon 900 intelligent driving system, aiming to set a new benchmark for "oil-electric intelligence" [3] - The Falcon 900 system utilizes a self-developed VLA model that integrates visual perception, language understanding, and action execution [3] - The model has been trained on 20 million kilometers of real-world data, capable of understanding over 5000 traffic scenarios, achieving a 92% accuracy rate in recognizing non-standard traffic signals in complex urban conditions, a 37% improvement over traditional systems [3] Group 4: Geely Automobile - Geely is actively developing VLA technology, integrating it with world models to create a comprehensive world model system [4] - The Qianli Haohan system features a "dual end-to-end model" design, enabling a multi-modal VLA general scene model and an end-to-end model to back each other up [4] - This system is powered by dual NVIDIA Thor chips, with a total computing power of 1400 TOPS and over 40 perception units capable of detecting objects 0.75 meters in size from 300 meters away [4] Group 5: Yuanrong Qihang - Yuanrong Qihang is also investing in the VLA model, with five models expected to feature it by the third quarter of this year [5] - The company was among the earliest to publicly announce its VLA development in June of last year [5] - The VLA model focuses on defensive driving with four core functions: spatial semantic understanding, recognition of irregular obstacles, comprehension of text-based guide signs, and voice control of the vehicle, which will be gradually released with mass production [5]

虽然触觉传感器如此重要，但还有很多问题没有解决，比如分辨率不高，实时性保证不了、买过来没多久 就坏了、质量不行等。然而，我们发现现场有一家触觉传感器硬件公司同时在分辨率、实时性、耐用性与 成本平衡方面取得了最优，这家公司就是 " 戴盟机器人 "。 这几天去WRC25逛了一圈，看到了各家具身机器人公司的产品和功能。说实话相比于去年，硬件和技术上 真的是有较大提升。还看到了多家没去WAIC25现场的公司，总体结论是现阶段的本体已经基本能够满足一 些场景的需求，反而是感知大脑，有点落后于硬件。 现场看到了很多相关的技术，特别是VLA模型。VLA作为新一代端到端视觉语言动作模型，是各家公司与 研究机构重点关注的。不过在展示过程中我们也发现了一个明显的问题，视觉虽然能提供丰富的环境信 息，在涉及物理交互（如抓取、操作物体）时，无法精确感知物体的材质、硬度、摩擦力等属性。特别是 在工业装配、医疗手术、家庭服务等场景中，机器人需要执行高精度任务，如果不小心用力过度将会产生 不良的后果。 就在近几天，戴盟机器人也（Daimon Robotics）宣布完成亿元级天使++轮融资，由招商局创投领投，东方 嘉富、架桥资本跟投。本轮融 ...

Group 1 - The establishment of a technical exchange group focused on embodied intelligence technologies, including VLA, VLN, remote operation, Diffusion Policy, reinforcement learning, VLA+RL, sim2real, multimodal large models, simulation, motion control, target navigation, mapping and localization, and navigation [1] - Interested individuals can add the assistant's WeChat AIDriver005 to join the community [2] - To expedite the joining process, it is recommended to include the organization/school, name, and research direction in the remarks [3]

智能涌现 . 直击AI新时代下涌现的产业革命。36氪旗下账号。 具身智能创业，要做苹果，而不是安卓。 文 ｜ 邱晓芬 编辑 ｜ 苏建勋 来源｜ 智能涌现（ID：AIEmergence） 封面来源 ｜ 视觉中国 不管是刚刚结束的WAIC（世界人工智能大会），还是本周要开幕的WRC（世界机器人大会），如何在展会上识别一个机器人的真正实力？ 具身智能公司"千寻智能"的联合创始人高阳，提供了这样几个tips： 以下文章来源于智能涌现 ，作者邱晓芬 对于号称能叠衣服的机器人，你可以尝试把衣服团成一团，随意丢在桌上，观察它是否能继续完成动作；或者是再给它裤子、外套，看它能否具备跨品类 的泛化能力； 在机器人操作时，可以观察其动作是否足够丝滑流畅，而不是一卡一卡，这代表了思维和动作的协调性…… 给我们提出指引的高阳，是当前具身智能领域炙手可热的创业者之一——从美国加州大学伯克利分校博士毕业后，他选择回国成为清华大学交叉信息研究 院助理教授。 2023年，他又与前珞石机器人CTO韩峰涛一起，创办了具身智能公司千寻智能——韩峰涛硬件经验丰富，过往操盘过数万台机器人量产出货，高阳则有 AI的研究基础，学术和产业界的搭配，使得千寻 ...

Core Viewpoint - The article discusses the emergence of embodied intelligence in robotics, emphasizing the importance of creating integrated hardware and software solutions, akin to Apple's approach, rather than a fragmented one like Android's [5][6]. Group 1: Company Overview - Qianxun Intelligent, co-founded by Gao Yang and Han Fengtao, has raised over 1 billion RMB in funding within 19 months, with investors including Huawei Hubble, JD.com, and CATL [4]. - Gao Yang, a former assistant professor at Tsinghua University, transitioned from academia to entrepreneurship, highlighting the challenges and learning experiences in this shift [5][12]. Group 2: Market Insights - The robotics market is currently competitive, with established companies focusing on hardware while neglecting the software aspect, which Gao Yang believes is crucial for long-term success [9]. - The potential for embodied intelligence is seen as inevitable, driven by advancements in AI technologies like ChatGPT, which have shifted perceptions about the capabilities of AI [8]. Group 3: Technical Perspectives - The integration of hardware and software is deemed essential in the early stages of robotics development, as seen in historical examples like IBM's approach to personal computers [6][7]. - Gao Yang emphasizes the importance of algorithms and data in evaluating the performance of robotic systems, noting that models must be capable of handling complex tasks rather than just simple ones [28][29]. Group 4: Future Outlook - The anticipated development of robots capable of performing complex tasks, referred to as Robot GPT-3.5, is expected to significantly enhance their functionality in everyday scenarios [32]. - The article suggests that the current focus on large-scale data collection in robotics may not be as valuable due to the rapid evolution of robot forms, indicating a need for more effective pre-training methods [41][42].

Group 1 - The establishment of the Embodied Intelligence Heart Technology Exchange Group focuses on various advanced technologies including VLA, VLN, remote operation, Diffusion Policy, reinforcement learning, VLA+RL, sim2real, multimodal large models, simulation, motion control, target navigation, mapping and localization, and navigation [1] - Interested individuals can add the assistant's WeChat AIDriver005 to join the community [2] - To expedite the joining process, it is recommended to include a note with the institution/school, name, and research direction [3]

Core Viewpoint - The article emphasizes the importance of preparing for non-technical interview questions in the autonomous driving industry, highlighting the need for candidates to articulate their interests, communication skills, and learning abilities effectively [6][10][11]. Group 1: Interview Preparation - Candidates should reflect on their interests and experiences to answer open-ended questions, as interviewers are often looking for personal insights and opinions [6][10]. - Communication skills are crucial; candidates should demonstrate their ability to engage with mentors and express their thought processes when seeking guidance [6][10]. - Learning ability is assessed through candidates' approaches to acquiring new technical knowledge, emphasizing the importance of establishing a comprehensive understanding before diving into specifics [7][10]. Group 2: Community and Resources - The "Autonomous Driving Heart Knowledge Planet" community provides a platform for technical exchange, featuring members from renowned universities and leading companies in the autonomous driving sector [23][11]. - The community offers a wealth of resources, including over 40 technical routes and numerous open-source projects, aimed at facilitating learning and career development in the autonomous driving field [11][19]. - Members can access job opportunities and industry insights, fostering a complete ecosystem for autonomous driving professionals [21][22]. Group 3: Learning and Development - The community has curated a comprehensive learning path for beginners and advanced researchers, covering various aspects of autonomous driving technology [17][19]. - Regular discussions and Q&A sessions are held to address common industry challenges and share knowledge on emerging technologies [24][87]. - The platform also features live sessions with industry experts, providing members with direct access to cutting-edge research and practical applications [86][11].