Core Viewpoint - The article introduces AutoRobo Knowledge Planet, a job-seeking community focused on autonomous driving and embodied intelligence, aimed at helping students quickly match with suitable positions and prepare for interviews [1][3]. Group 1: Community Overview - AutoRobo Knowledge Planet is a platform for job seekers in the fields of autonomous driving, embodied intelligence, and robotics, currently hosting nearly 1,000 members from various companies such as Horizon, Li Auto, Huawei, and Xiaomi [3]. - The community includes both experienced professionals and students preparing for upcoming job fairs in 2024 and 2025, covering a wide range of topics related to autonomous driving and embodied intelligence [3]. Group 2: Content and Resources - The platform provides a wealth of resources including interview questions, interview experiences, industry reports, salary negotiation tips, and services for resume optimization and internal referrals [3][5]. - AutoRobo has compiled a comprehensive list of 100 questions related to autonomous driving and embodied intelligence, which are essential for job seekers [9][10][13]. Group 3: Industry Reports - The community offers various industry reports to help members understand the current state, development trends, and market opportunities within the autonomous driving and embodied intelligence sectors [16][17]. - Reports include topics such as the World Robotics Report, Chinese Embodied Intelligence Venture Capital Report, and in-depth studies on the development of humanoid robots [17]. Group 4: Interview Experiences - The platform shares both successful and unsuccessful interview experiences across different roles, providing insights from various companies and positions, including algorithm engineering and product management [19][20]. - This collection of experiences aims to help members learn from past candidates' journeys and avoid common pitfalls during the interview process [20][21]. Group 5: Salary Negotiation and Professional Development - AutoRobo provides guidance on salary negotiation techniques and common HR questions to prepare members for discussions regarding compensation [22][25]. - The community also shares foundational resources, including recommended books and skills necessary for roles in robotics, autonomous driving, and AI [23][24].

Core Insights - The article discusses the evolving landscape of embodied intelligence, highlighting its transition from a period of hype to a more measured approach as the technology matures and is not yet at a productivity stage [2]. Group 1: Industry Trends - Embodied intelligence has gained significant attention over the past few years, but the industry is now recognizing that it is still in the early stages of development [2]. - There is a growing demand for skills in multi-sensor fusion and robotics, particularly in areas like SLAM and ROS, which are crucial for engaging with embodied intelligence [3][4]. - Many companies in the robotics sector are rapidly developing, with numerous startups receiving substantial funding, indicating a positive outlook for the industry in the coming years [3][4]. Group 2: Job Market and Skills Development - The job market for algorithm positions is competitive, with a focus on cutting-edge technologies such as end-to-end models, VLA, and reinforcement learning [3]. - Candidates with a background in robotics and a solid understanding of the latest technologies are likely to find opportunities, especially as traditional robotics remains a primary product line [4]. - The article encourages individuals to enhance their technical skills in robotics and embodied intelligence to remain competitive in the job market [3][4]. Group 3: Community and Resources - The article promotes a community platform that offers resources for learning about autonomous driving and embodied intelligence, including video courses and job postings [5]. - The community aims to gather a large number of professionals and students interested in smart driving and embodied intelligence, fostering collaboration and knowledge sharing [5]. - The platform provides access to the latest industry trends, technical discussions, and job opportunities, making it a valuable resource for those looking to enter or advance in the field [5].

Core Viewpoint - GeoScan S1 is presented as a highly cost-effective handheld 3D laser scanner, designed for various operational fields with features such as lightweight design, one-button operation, and centimeter-level precision in real-time 3D scene reconstruction [1][4]. Group 1: Product Features - The GeoScan S1 can generate point clouds at a rate of 200,000 points per second, with a maximum measurement distance of 70 meters and 360° coverage, supporting large scenes over 200,000 square meters [1][23]. - It integrates multiple sensors, including RTK, 3D laser radar, and dual wide-angle cameras, allowing for high precision and efficiency in mapping [7][28]. - The device operates on a handheld Ubuntu system and includes various sensor devices, with a power supply integrated into the handle [2][21]. Group 2: Usability and Efficiency - The device is designed for ease of use, with a simple one-button start for scanning operations and immediate usability of the exported results without complex deployment [3][4]. - It features a small tilt angle design (25°) for the laser radar, enhancing efficiency by covering multiple orientations without repeated data collection [9][23]. - The GeoScan S1 supports real-time modeling and high-fidelity restoration through multi-sensor fusion and microsecond-level data synchronization [21][28]. Group 3: Market Position and Pricing - The product is positioned as the most cost-effective option in the market, with a starting price of 19,800 yuan for the basic version and higher for additional features [4][51]. - The company emphasizes its strong background and project validation, having collaborated with academic institutions and completed numerous projects [3][4]. Group 4: Application Scenarios - GeoScan S1 is suitable for various environments, including office buildings, parking lots, industrial parks, tunnels, forests, and mines, enabling precise 3D scene mapping [32][40]. - The device can be integrated with drones, unmanned vehicles, and other platforms for automated operations [38][40].

Core Viewpoint - The article discusses the advancements and applications of Goal-Oriented Navigation technology, emphasizing its significance in enabling robots to autonomously navigate and make decisions in unfamiliar environments, moving from traditional instruction-based navigation to a more autonomous understanding of the world [1][2]. Group 1: Technology Overview - Goal-Oriented Navigation is a key area within embodied navigation, relying on three main technological pillars: language understanding, environmental perception, and path planning [1]. - The technology has been successfully implemented in various verticals, including delivery, healthcare, and hospitality, showcasing its ability to adapt to dynamic environments and human interactions [2]. - The evolution of Goal-Oriented Navigation can be categorized into three generations: end-to-end methods, modular approaches, and LLM/VLM integration strategies [4][6]. Group 2: Industry Applications - In delivery scenarios, Goal-Oriented Navigation combined with social navigation algorithms allows robots to perform tasks in complex urban settings, as seen with Meituan's delivery vehicles and Starship Technologies' campus robots [2]. - In healthcare and hospitality, companies like Aethon and Jianneng Technology have deployed service robots for autonomous delivery of medications and meals, enhancing service efficiency [2]. - The integration of Goal-Oriented Navigation in humanoid robots is accelerating their penetration into home services, care, and industrial logistics [2]. Group 3: Technical Progress and Challenges - The development of embodied navigation has seen significant advancements since the introduction of PointNav in 2020, with evaluation systems expanding to include ImageNav and ObjectNav [3]. - Current challenges include achieving human-level performance in open vocabulary object navigation and dynamic obstacle scenarios, despite notable progress in closed-set tasks [3]. - The introduction of frameworks like Sim2Real by Meta AI provides methodologies for transitioning from simulation training to real-world deployment [3]. Group 4: Educational Initiatives - The article highlights the creation of a comprehensive course aimed at addressing the challenges faced by newcomers in the field of Goal-Oriented Navigation, focusing on practical applications and theoretical foundations [9][10][11]. - The course structure includes a systematic approach to understanding the technology's evolution, practical training on simulation platforms, and hands-on projects to bridge theory and practice [14][15][16][18].

Core Insights - The article presents a novel framework called Human2LocoMan for enhancing quadrupedal robots' manipulation capabilities through human pretraining, addressing the challenges of autonomous multi-functional operations in complex environments [5][9][38] - The framework utilizes a modular cross-entity transformer architecture (MXT) to facilitate effective data collection and transfer learning from human demonstrations to robotic strategies, demonstrating significant performance improvements in various tasks [10][36] Group 1: Framework and Methodology - The Human2LocoMan framework integrates remote operation and data collection systems to bridge the action space between humans and quadrupedal robots, enabling efficient acquisition of high-quality datasets [9][38] - The system employs extended reality (XR) technology to capture human actions and translate them into robotic movements, enhancing the robot's workspace and perception capabilities [9][12] - A modular design in the MXT architecture allows for the sharing of a common transformer backbone while maintaining entity-specific markers, facilitating effective strategy transfer across different robotic entities [16][37] Group 2: Experimental Results - Experiments conducted on six challenging household tasks showed an average success rate improvement of 41.9% and an 82.7% increase in out-of-distribution (OOD) scenarios when using human data for pretraining [6][10] - The framework demonstrated robust generalization capabilities, maintaining high performance even with limited robotic data, and significantly improving task execution in both ID and OOD scenarios [37][38] - The modular design of MXT was shown to outperform traditional methods, indicating its effectiveness in leveraging human data for enhanced robotic learning and performance [33][36] Group 3: Data Collection and Efficiency - The Human2LocoMan system allows for efficient data collection, achieving over 50 robotic trajectories and 200 human trajectories within 30 minutes, showcasing its potential for rapid data acquisition in complex tasks [30] - The framework supports a variety of operation modes, including single and dual-hand tasks, and is adaptable to different object types and scenarios, enhancing its applicability across various domains [30][36]

Core Viewpoint - The article discusses the rapid advancements in the autonomous driving field, emphasizing the increasing demand for transparency and interpretability in decision-making modules of autonomous systems. It highlights the limitations of both data-driven and rule-based decision systems and introduces a novel framework called ADRD, which leverages large language models (LLMs) to enhance decision-making capabilities in autonomous driving [1][2][26]. Summary by Sections 1. Introduction - The autonomous driving sector has seen significant progress, leading to a heightened focus on the interpretability of decision-making processes within these systems. The reliance on deep learning methods has raised concerns regarding performance in non-distributed driving scenarios and the complexity of decision logic [1]. 2. Proposed Framework - The ADRD framework is introduced as a solution to the challenges faced by traditional decision systems. It combines rule-based decision-making with the capabilities of LLMs, demonstrating superior performance in various driving scenarios compared to conventional methods [2][26]. 3. Algorithm Model and Implementation Details - The ADRD model consists of three main modules: information, agent, and testing. The information module converts driving rules and environmental data into natural language for LLM processing. The agent module includes a planner, encoder, and summarizer, which work together to ensure stable reasoning and effective feedback loops [5][7][13]. 4. Experimental Results - Experiments conducted in the Highway-env simulation environment show that ADRD outperforms traditional methods in terms of average safe driving time and reasoning speed across various driving conditions. For instance, in a normal density scenario, ADRD achieved an average driving time of 25.15 seconds, significantly higher than other methods [21][22]. 5. Conclusion - The article concludes that the ADRD framework effectively utilizes LLMs to generate decision trees for autonomous driving, outperforming both traditional reinforcement learning and knowledge-driven models in performance, response speed, and interpretability [26].

Core Viewpoint - The article discusses the instability of talent within the autonomous driving sector, particularly focusing on a new player in the industry that is experiencing significant personnel changes in its core technology teams, which may impact its technological advancements and competitive edge [3][5][6]. Group 1: Talent Instability - A key figure responsible for the development of the VLA model at a new player in the autonomous driving sector is currently on sick leave, raising concerns about the impact on the company's research and development efforts [3]. - The core team for autonomous driving at this new player is unstable, with the heads of the end-to-end and world model departments having left or taken leave, leaving only the production head in place [5]. - The industry has seen a trend of frequent talent turnover, especially among companies that have previously excelled, leading to a lack of stability and continuity in technological development [6][7]. Group 2: Leadership and Management Response - Despite the instability in key personnel, the leadership of the new player remains optimistic about achieving significant advancements once new models are produced, indicating a disconnect between management confidence and the reality of talent challenges [5]. - There is a noted lack of effort from company leaders to address the talent turnover issue, suggesting a belief that technological changes necessitate a shift in personnel [8]. - The phenomenon of treating talent as expendable resources has emerged, leading to a short value cycle for employees, who often seek to leave after project completion for better opportunities [10]. Group 3: Industry Trends - The article highlights a broader trend in the autonomous driving industry where companies cycle through different teams for various technological iterations, indicating a lack of long-term investment in talent [9]. - The departure of skilled professionals from the autonomous driving sector to pursue opportunities in robotics or other fields reflects a growing desire for autonomy and control over one's career path [10].

Core Insights - The article discusses the rapid development of large language models (LLMs) and multimodal models, focusing on enhancing model efficiency, expanding knowledge capabilities, and improving reasoning performance as core issues in current AI research [1][2]. Course Overview - The course systematically explores cutting-edge optimization methods for large models, emphasizing three key areas: parameter-efficient computation, dynamic knowledge expansion, and complex reasoning [1]. - It addresses core challenges in model optimization, including lightweight methods such as pruning, sparsification, and quantization for parameter compression; dynamic knowledge injection techniques like retrieval-augmented generation (RAG) and parameter-efficient fine-tuning (PEFT) for knowledge expansion; and advanced reasoning paradigms such as chain-of-thought (CoT) and reinforcement learning optimization (GRPO) for reasoning enhancement [1]. Course Objectives - The course aims to help students systematically master key theoretical knowledge in specified directions and develop a clearer understanding of the content [5]. - It seeks to bridge the gap for students who lack direction and practical skills, enabling them to combine theoretical knowledge with coding practice and lay the groundwork for developing new models [5]. - The course also focuses on improving students' academic writing skills, providing guidance on manuscript preparation and submission [5]. Target Audience - The course is designed for master's and doctoral students in the field of large models, those seeking to enhance their resumes for graduate studies abroad, and professionals in the AI field looking to systematically improve their algorithmic theory and writing skills [6]. Admission Requirements - Basic requirements include a foundational understanding of deep learning/machine learning, familiarity with Python syntax, and experience with PyTorch [7]. Course Structure - The course consists of 12 weeks of online group research followed by 2 weeks of paper guidance, culminating in a 10-week paper maintenance period [11]. - Students will analyze classic and cutting-edge papers, understand key algorithms and principles, and develop their research ideas [11]. Weekly Breakdown - The course covers various topics, including model pruning, quantization, dynamic knowledge expansion, advanced reasoning techniques, and multimodal understanding [16][18]. - Each week includes specific themes and outputs, such as determining research ideas, optimizing model size and performance, and enhancing coding capabilities [16][18]. Additional Resources - The course provides access to datasets from public sources and baseline code tailored to specific applications [13][14]. - Essential papers and resources are recommended for foundational knowledge and advanced techniques in model optimization [15][17].

点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近15个 方向 学习 路线 对于26届毕业的算法小伙伴，这个时候应该找到实习了。 如果不是特别满意的实习岗位，往往很容易顾虑。一方面没有实习怕影响秋招，另一方面可能去了也学 不到东西。。。 我们在求职星球里回答很多类似的问题，对于没有实习经历的小伙伴来说，大多数我都建议一定要去实 习，即使方向不对口，也是比没有实习经历强一些的。 以下的一些小伙伴在 AutoRobo知识星球 的星友咨询的问题： 星主您好～冒昧打扰您啦。我是双非本，九硕，电子信息类。想来咨询一下关于实习选择的问题。在实 验室做过一些3D感知、目标检测、分割相关的工作，只能说跑通过开源算法，没有深入的去优化过。平 时老板也是比较放养的状态，加上自己也比较松散，现在研二准备实习有些慌。匆匆忙忙投递实习简 历，最近拿到了一些主机厂的实力，是后处理和数据岗位，怕过去就是打打杂，想问问您的建议~ 星主答：建议还是去实习实习，同时还要补补前沿自动驾驶算法的内容，比如大模型、世界模型、扩散 模型、强化学习等等。既然已经意识到自己的短版，就不能在不拼一把。挤一挤时间这几个月冲刺一下 总比 ...

Core Viewpoint - The article discusses the development and implementation of the Street Gaussians algorithm for dynamic scene representation in autonomous driving, highlighting its efficiency in training and rendering compared to previous methods [2][3]. Group 1: Background and Challenges - Previous methods faced challenges such as slow training and rendering speeds, as well as inaccuracies in vehicle pose tracking [3]. - Street Gaussians aims to generate realistic images for view synthesis in dynamic urban street scenes by modeling them as a combination of foreground moving vehicles and static backgrounds [3][4]. Group 2: Technical Implementation - The background model is represented as a set of points in world coordinates, each assigned a 3D Gaussian to represent geometry and color, with parameters optimized to avoid invalid values [8]. - The object model for moving vehicles includes a set of optimizable tracking poses and point clouds, with similar Gaussian attributes to the background model but defined in local coordinates [11]. - A 4D spherical harmonic model is introduced to encode temporal information into the appearance of moving vehicles without high storage costs [12]. Group 3: Initialization and Data Handling - Street Gaussians utilizes aggregated LiDAR point clouds for initialization, addressing the limitations of traditional SfM point clouds in urban environments [17]. - For objects with fewer than 2,000 LiDAR points, random sampling is employed to ensure sufficient data for model initialization [17]. Group 4: Course and Learning Opportunities - The article promotes a specialized course on 3D Gaussian Splatting (3DGS), covering various subfields and practical applications in autonomous driving, aimed at enhancing understanding and implementation skills [26][35].