Core Insights - The article highlights significant advancements in hardware and technology for robotics, particularly in tactile sensing, which is crucial for precise physical interactions in various applications [1][3][10] - Daimon Robotics has achieved a breakthrough in tactile sensor technology, addressing key issues such as resolution, real-time performance, and durability, which are critical for the industry's growth [2][9] Group 1: Technology Advancements - The VLA model (Visual-Language-Action) is a focus for many companies, but there are limitations in physical interaction capabilities, necessitating the integration of tactile sensing to enhance performance [1] - Daimon Robotics has developed a new high-resolution visual-tactile sensing technology that captures minute optical changes, enabling robots to possess human-like tactile perception [4][10] - The DM-Tac W sensor, a pioneering product, features 40,000 sensing units per square centimeter, significantly surpassing human capabilities and traditional sensors [4][9] Group 2: Product Development - Daimon Robotics has introduced the DM-Hand1, a dexterous robotic hand that integrates ultra-thin visual-tactile sensors, enhancing flexibility and precision in tasks such as delicate handling and assembly [6] - The company showcased its products at the World Robot Conference (WRC), demonstrating their practical applications and attracting significant interest from attendees [8] Group 3: Market Position and Future Outlook - Daimon Robotics has successfully completed a significant financing round, raising hundreds of millions, which will be used to further develop and commercialize its tactile sensing technologies [3][10] - The company has transitioned from prototype development to large-scale production, achieving certifications and passing extensive durability tests, positioning itself for commercial success in the tactile sensing market [9][10]

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].

Core Viewpoint - The article emphasizes the convergence of autonomous driving technology, highlighting the shift from numerous diverse approaches to a more unified model, which indicates higher technical barriers in the industry [1] Group 1 - The industry is moving towards a unified solution with models like one model, VLM, and VLA, suggesting a reduction in the need for numerous algorithm engineers [1] - The article encourages the establishment of a large community to support industry professionals, facilitating growth and collaboration among peers [1] - A new job-related community is being launched to discuss industry trends, company developments, product research, and job opportunities [1]

Core Viewpoint - End-to-end (E2E) autonomous driving is currently the core algorithm for mass production in intelligent driving, with significant advancements in the VLM/VLA systems leading to high demand for related positions and salaries reaching up to 1 million annually [2][11]. Group 1: Industry Trends - The concept of E2E has evolved significantly, with various technical schools emerging, yet many still struggle to understand its workings and distinctions between single-stage and two-stage approaches [2][4]. - The introduction of VLA (Vision-Language Architecture) is seen as a new frontier in autonomous driving, with companies actively researching and developing new generation mass production solutions [21][22]. Group 2: Educational Initiatives - A new course titled "End-to-End and VLA Autonomous Driving" has been launched to address the challenges faced by newcomers in the field, focusing on practical applications and theoretical foundations [14][27]. - The course aims to provide a comprehensive understanding of E2E autonomous driving, covering various models and methodologies, including diffusion models and reinforcement learning [6][19][21]. Group 3: Job Market Insights - The job market for VLA/VLM algorithm experts is robust, with salaries for positions requiring 3-5 years of experience ranging from 40K to 70K monthly, indicating a strong demand for skilled professionals [11][12]. - Positions such as VLA model quantization deployment engineers and multi-modal VLA model direction experts are particularly sought after, reflecting the industry's shift towards advanced algorithmic solutions [11][12].

Core Viewpoint - The article emphasizes the establishment of a leading communication platform for autonomous driving technology in China, focusing on industry, academic, and career development aspects [1]. Group 1 - The platform, named "Autonomous Driving Heart," aims to facilitate discussions and exchanges among professionals in various fields related to autonomous driving technology [1]. - The technical discussion group covers a wide range of topics including large models, end-to-end systems, VLA, BEV perception, multi-modal perception, occupancy, online mapping, 3DGS, multi-sensor fusion, transformers, point cloud processing, SLAM, depth estimation, trajectory prediction, high-precision maps, NeRF, planning control, model deployment, autonomous driving simulation testing, product management, hardware configuration, and AI job exchange [1]. - Interested individuals are encouraged to join the community by adding a WeChat assistant and providing their company/school, nickname, and research direction [1].

Group 1 - The article highlights the growing anxiety among job seekers, particularly students and professionals looking to transition into new fields, driven by the desire for better opportunities [1] - It notes that the landscape of autonomous driving technology is becoming more standardized, with a shift from numerous directions requiring algorithm engineers to a focus on unified models like one model, VLM, and VLA, indicating higher technical barriers [1] - The article emphasizes the importance of community building to support individuals in their career growth and industry knowledge, leading to the establishment of a job-related community for discussions on industry trends, company developments, and job opportunities [1]