编辑 丨 宋玮 "机器人得先通用，才能进家庭，不然很容易把机器人变成另一个扫地机" 文 丨 李梓楠 郭彦东是那种最正儿八经的机器人创业者，学习成绩好、技术强，有漂亮的工作履历，大厂管过大研 发，做过软件也做过硬件，是这轮具身智能创业热潮从大公司离职创业级别最高的技术高管。 郭彦东从小到大都是第一，高考数学单科满分上的北邮。后来他考上美国普渡大学的博士，学人工智 能。他选最严厉的院士导师，零下三十度，在普渡大学的玉米地里采数据做实验。他说他不知道打 B 是 几分，他只有 A 和 A+。 博士毕业后，他终于遇到成绩比他更好的人。加入微软美国研究院，公司有 5 个是图灵奖得主。郭彦东 压力很大，师兄安慰他说，"别害怕，你去了你也会和他们一样强。" 郭彦东在谈到这段经历时哽咽了， 他在微软度过了一段充满理想主义的时光，微软也养成了他平视技术大佬的习惯，"重要的是技术自 信，大佬也会出错。" 2018 年，郭彦东想把深度学习理念应用到汽车上，于是从微软离职加入创业初期的小鹏汽车。2020 年，他又加入 OPPO 任首席科学家。小鹏和 OPPO 教会他经营理念。何小鹏告诉他，做 to b 软件，只 能卖 1 块钱，做好软 ...

Core Viewpoint - The article discusses the decision-making process for individuals in the autonomous driving field regarding whether to pursue a PhD, continue working, or switch careers, emphasizing the importance of foundational knowledge and practical experience in the industry [2][3]. Group 1: Career Decisions - The article highlights two critical questions for individuals considering a career in autonomous driving: the availability of foundational knowledge and practical experience in their current environment, and their readiness to take on pioneering research roles if pursuing a PhD [2][3]. - It points out that many academic mentors may lack deep expertise in autonomous driving, which can hinder students' development if they do not have a solid foundation [2]. - The article suggests that students should assess their preparedness to independently explore and solve problems, especially in cutting-edge research areas where few references exist [2][3]. Group 2: Community and Resources - The "Autonomous Driving Heart Knowledge Planet" community is introduced as a resource for beginners, offering a comprehensive platform for learning, sharing knowledge, and networking within the autonomous driving field [3][5]. - The community has over 4,000 members and aims to grow to nearly 10,000 in the next two years, providing a space for technical sharing and job-seeking interactions [3][5]. - Various practical questions and topics are addressed within the community, including entry points for end-to-end systems, multi-modal models, and the latest industry trends [5][16]. Group 3: Learning and Development - The community offers a structured learning system with over 40 technical routes covering various aspects of autonomous driving, including perception, simulation, and planning control [7][14]. - It provides access to numerous resources, including video tutorials, technical discussions, and job opportunities, aimed at both beginners and those looking to advance their skills [8][18]. - The community also facilitates connections with industry leaders and experts, enhancing members' understanding of the latest developments and job market trends in autonomous driving [12][92].

Group 1 - The humanoid robot industry is experiencing a dichotomy, with significant advancements in practical applications contrasted by challenges in scaling production and securing orders [1][5][36] - Investment in humanoid robotics has surged, with over 20 companies in the sector pursuing IPOs, marking a transformative year for mass production of humanoid robots [1][5] - The development of embodied intelligence is at a crossroads, requiring a balance between technological innovation and practical profitability [1][15] Group 2 - Companies like Beijing Galaxy General Robotics are leading the way in deploying humanoid robots in various sectors, achieving significant milestones in industrial and retail applications [5][8] - The key challenge for humanoid robots lies in their ability to operate autonomously without remote control, which is dependent on advanced data and model training [10][12] - High-quality data is crucial for enhancing the capabilities of humanoid robots, with a focus on diverse and rich datasets to improve their performance in real-world scenarios [12][30] Group 3 - The success of humanoid robots in competitive environments, such as soccer, demonstrates their potential for real-world applications and helps in refining their operational capabilities [36][41] - The industry faces a "chicken or egg" dilemma, where technological advancements must align with market demand to create a sustainable business model [37][42] - The transition from demonstration to practical application is essential for the industry, with a focus on creating a commercial ecosystem that supports ongoing development and deployment [35][42]

Core Viewpoint - The article discusses a research guidance program focused on Vision-Language-Action (VLA) models for autonomous driving, aimed at helping students develop their research skills and produce publishable papers in the field [5][36]. Group 1: Program Overview - The VLA research guidance program includes 12 weeks of online group research, 2 weeks of paper guidance, and 10 weeks of paper maintenance [15][36]. - The program addresses common issues faced by students, such as lack of direction, poor hands-on skills, and difficulties in writing and submitting papers [38]. Group 2: Course Structure - The course is structured into 14 weeks, covering topics from introductory lessons to advanced VLA models and paper writing methodologies [10][12][37]. - Key topics include traditional end-to-end autonomous driving, modular VLA models, and reasoning-enhanced VLA models [10][12][36]. Group 3: Target Audience and Requirements - The program targets students at various academic levels (bachelor's, master's, and doctoral) who are interested in enhancing their research capabilities in autonomous driving and AI [16][36]. - Basic requirements include familiarity with deep learning, Python programming, and the use of PyTorch [22][36]. Group 4: Course Benefits - Participants will gain insights into classic and cutting-edge papers, coding skills, and methodologies for writing and revising papers [21][36]. - The program aims to provide each student with a research idea, enhancing their ability to conduct independent research [21][36]. Group 5: Teaching Methodology - The program employs a "2+1" teaching model, featuring a main instructor and additional support staff to ensure comprehensive learning [24][25]. - Continuous assessment and feedback mechanisms are in place to optimize the learning experience and address individual student needs [25][36].