Core Viewpoint - The article highlights the rapid advancements in AI technologies, particularly in autonomous driving and embodied intelligence, which have significantly influenced the industry and investment landscape [1]. Group 1: AutoRobo Knowledge Community - AutoRobo Knowledge Community is established as a platform for job seekers in the fields of autonomous driving, embodied intelligence, and robotics, currently hosting nearly 1000 members from various companies [2]. - The community provides resources such as interview questions, industry reports, salary negotiation tips, and resume optimization services to assist members in their job search [2][3]. Group 2: Recruitment Information - The community regularly shares job openings in algorithms, development, and product roles, including positions for campus recruitment, social recruitment, and internships [3][4]. Group 3: Interview Preparation - A compilation of 100 interview questions related to autonomous driving and embodied intelligence is available, covering essential topics for job seekers [6]. - Specific areas of focus include sensor fusion, lane detection algorithms, and various machine learning deployment techniques [7][12]. Group 4: Industry Reports - The community offers access to numerous industry reports that provide insights into the current state, development trends, and market opportunities within the autonomous driving and embodied intelligence sectors [13][14]. - Reports include analyses of successful and failed interview experiences, which serve as valuable learning tools for members [15]. Group 5: Salary Negotiation and Professional Development - The community emphasizes the importance of salary negotiation skills and provides resources to help members navigate this aspect of their job search [17]. - A collection of recommended books related to robotics, autonomous driving, and AI is also available to support professional development [18].

什么是显示端到端VLA，提到显示，这一点和隐式是对立的。上一期分享，我们分享了隐式端到端的模型定 义，显示端到端VLA模型视频生成GOAL，明确生成了未来机械臂如何运动的图像！可以浏览下图！ 其中还涉及了一个比较重要的概念：逆运动学。 逆运动学 逆运动学主要应用在机器人学、动画学和计算机图形学中，与经典运动学相对。它的目标是根据目标位置，计 算物体（如机械臂或骨骼系统）的各个关节应该如何运动才能到达该目标。 列入在机器人领域，逆运动学会回答这样的实际问题：机械臂的末端（手爪）需要到达某个指定位置，那么每 个关节应该如何旋转。 逆运动学的核心步骤： 已知信息： 求解： 利用矩阵、三角学或迭代方法，计算每个关节的角度或未知，使得末端能够到达目标点。 多解性问题： 逆运动学通用会有多个解（甚至没解），需要在可能的解中选择一个最优解（如最小能量消耗或最自然运 动）。 主要工作一览 3）LAPA 1）开山之作：UniPi 将序列决策问题转化为文本条件视频生成问题：给定文本编码的目标描述，规划器会合成一组未来帧来描绘其 计划执行的行动序列，随后从生成的视频中提取控制动作。通过以文本作为底层目标描述，我们能够自然而然 地实 ...

Core Insights - The article discusses the unprecedented turning point in AI development, highlighting the rise of embodied intelligence, which allows machines to understand language, navigate complex environments, and make intelligent decisions [1][2]. Group 1: Embodied Intelligence - Embodied intelligence is defined as AI systems that not only possess a "brain" but also have a "body" capable of perceiving and interacting with the physical world [1]. - Major tech companies like Tesla, Boston Dynamics, OpenAI, and Google are competing in this transformative field, which is expected to revolutionize various industries including manufacturing, healthcare, and space exploration [1]. Group 2: Technical Challenges - Achieving true embodied intelligence faces significant technical challenges, requiring advanced algorithms and a deep understanding of physical simulation, robot control, and perception fusion [2][4]. - MuJoCo (Multi-Joint dynamics with Contact) is identified as a key technology in this domain, serving as a high-fidelity training environment for robot learning [4][8]. Group 3: MuJoCo's Role - MuJoCo allows researchers to create realistic virtual robots and environments, enabling millions of trials and learning experiences without the risk of damaging expensive hardware [6][4]. - The simulation speed can be hundreds of times faster than real-time, significantly accelerating the learning process [6]. - MuJoCo has become a standard tool in both academia and industry, with major companies utilizing it for robot research [8]. Group 4: Practical Training - A comprehensive MuJoCo development course has been designed, focusing on practical applications and theoretical foundations, covering topics from physical simulation to deep reinforcement learning [9][10]. - The course is structured into six modules, each with specific learning objectives and practical projects, ensuring a solid grasp of the technology stack [13][16]. Group 5: Project-Based Learning - The course includes six progressively challenging projects, such as building a robotic arm control system and implementing vision-guided grasping [19][21]. - Each project is designed to reinforce theoretical concepts through hands-on experience, ensuring participants understand both the "how" and "why" of the technology [29][33]. Group 6: Target Audience and Outcomes - The course is suitable for individuals with programming or algorithm backgrounds looking to enter the field of embodied robotics, as well as students and professionals interested in enhancing their practical skills [30][32]. - Upon completion, participants will have a complete technology stack in embodied intelligence, gaining advantages in technical, engineering, and innovation capabilities [32][33].

Core Insights - The article discusses the limitations of current Vision-Language Models (VLMs) in spatial reasoning tasks, highlighting the need for improved datasets and methodologies to enhance performance in various scenarios [3][12]. Dataset Limitations - The existing InternSpatial dataset has three main limitations: 1. Limited scene diversity, focusing primarily on indoor and outdoor environments, lacking diverse contexts like driving and embodied navigation [3]. 2. Restricted instruction formats, only supporting natural language or region masks, which do not encompass the variety of queries found in real-world applications [3]. 3. Lack of multi-view supervision, with over 90% of data focusing on single-image reasoning, failing to model spatiotemporal relationships across views [3]. Evaluation Benchmark - The InternSpatial-Bench evaluation benchmark includes 6,008 QA pairs across five tasks, assessing position comparison, size comparison, rotation estimation, object counting, and existence estimation [7]. - The benchmark also introduces 1,000 additional QA pairs for multi-view rotation angle prediction [7]. Data Engine Design - The data engine employs a three-stage automated pipeline: 1. Annotation generation using existing annotations or SAM2 for mask generation [9]. 2. View alignment to construct a standard 3D coordinate system [9]. 3. Template-based QA generation with predefined task templates [9]. Experimental Results - Spatial reasoning performance has improved, with InternVL-Spatial-8B showing a 1.8% increase in position comparison accuracy and a 17% increase in object counting accuracy compared to its predecessor [10]. - The model's performance across various tasks demonstrates significant enhancements, particularly in multi-view tasks [10]. Instruction Format Robustness - Current models exhibit a 23% accuracy drop when using the <box> format, while training with InternSpatial reduces the gap between different formats to within 5% [12]. - However, the automated QA generation struggles to replicate the complexity of natural language, indicating a need for further refinement [12].

目标驱动导航，赋予机器人自主完成导航目标 具身导航作为具身智能的核心领域，涉及语言理解、环境感知、路径规划三大技术支柱。目标驱动导航（Goal-Oriented Navigation）通过赋予机器人自主决策能 力，是具身导航中最具代表性的方向。 目标驱动导航要求智能体在陌生的三维环境中，仅凭目标描述（如坐标、图片、自然语言）等，即可自主完成环境探索与 路径规划。 与传统视觉语言导航（VLN）依赖显式指令不同，目标驱动导航系统需要实现从"听懂指令走对路"到"看懂世界自己找路"的跃迁：当人类下达"去厨房拿可乐"的指 令时，机器人需自主完成语义解析（识别厨房空间特征与可乐视觉属性）、环境建模（构建家居场景的空间拓扑）以及动态决策（避开移动的人类或宠物），这 背后凝聚着计算机视觉、强化学习与3D语义理解的交叉突破。 目标驱动导航技术已在多个垂直领域实现产业化落地。在终端配送场景中，该技术与社交导航算法结合，使机器人具备应对动态环境和人际交互的能力：美团无 人配送车通过动态路径重规划在复杂城市环境中执行递送任务，Starship Technologies的园区配送机器人已在欧美高校和社区部署。在医疗、酒店及餐饮场景，嘉 ...

Group 1 - The roundtable discussion focuses on the configurations of embodied intelligence and robotic arms, emphasizing the need for a deeper understanding of mechanical arm designs and their applications in various tasks [4][14][25] - Key topics include the practical experiences of guests with different robotic arm configurations, the requirements for robotic arms in terms of degrees of freedom, and the implications of these choices on technical routes and cost [4][14][25] - The discussion highlights the differences between six-axis and seven-axis robotic arms, addressing their respective advantages and disadvantages in specific use cases [27][29][41] Group 2 - The guests share insights on the importance of mechanical arm design in enhancing human-robot interaction, particularly in remote operation scenarios [8][36][41] - The conversation touches on the challenges posed by singularities in six-axis configurations and how seven-axis designs can mitigate these issues [40][47] - The role of human-like configurations in improving the usability and effectiveness of robotic arms is emphasized, suggesting that designs closer to human anatomy may facilitate better control and learning [30][35][38] Group 3 - The roundtable also discusses the trade-offs between simplicity and complexity in robotic arm designs, with a focus on how these choices impact data consistency and model training [34][52][58] - The guests explore the potential for using neural networks to enhance the performance of robotic arms, particularly in predicting trajectories and addressing singularities [40][57] - The conversation concludes with a reflection on the future of robotic arm development, suggesting that the industry may gravitate towards either simplified or human-like configurations based on task requirements [58][59]

最近收到了许多同学在论文发表上的求助，学校绕不开一篇三区论文硕士毕业，没有三篇CCF-A博 士都毕不了业，老师对这个新的方向不熟悉，开展不了工作。一直在为论文选题绞尽脑汁，实验设 计总遇瓶颈，写作逻辑混乱不清，投稿屡屡被拒！ 尤其是在前沿且复杂的自动驾驶、具身智能、机 器人领域，真的有点力不从心。 一篇好的论文需要有好的切入点，哪个方向更容易产出，这一个判断尤为重要！剩下的就是怎么论 证这个idea work，比当前SOTA有效（如果是A类会议）。实验的设计也非常重要，特别是消融实 验，要摸清是什么因素导致的提升。后期的写作技巧，取决于你是否能够让审稿人眼前一亮，如何 回复审稿意见也是需要经验的。 筹备了近1年，我们的论文辅导正式推出了，主要面向自动驾驶/具身智能/机器人领域。 我们是谁？ 国内最大的AI类技术自媒体平台，IP包含自动驾驶之心/具身智能之心/3D视觉之心等平台，拥有国内 最顶尖的学术资源。深耕 自动驾驶、具身智能、机器人 方向多年。我们深刻理解这些交叉学科的挑 战与机遇，更明白一篇高质量论文对于学生（尤其是硕博生）学业和未来发展的重要性。 我们目前有300+专职于自动驾驶/具身智能方向的老师。 ...

Core Viewpoint - Goal-Oriented Navigation empowers robots to autonomously complete navigation tasks based on goal descriptions, marking a significant shift from traditional visual language navigation systems [2][3]. Group 1: Technology Overview - Embodied navigation is a core area of embodied intelligence, relying on three technical pillars: language understanding, environmental perception, and path planning [2]. - Goal-Oriented Navigation requires robots to autonomously explore and plan paths in unfamiliar 3D environments using goal descriptions such as coordinates, images, or natural language [2]. - The technology has been industrialized across various verticals, including delivery, healthcare, hospitality, and industrial logistics, showcasing its adaptability and effectiveness [3]. Group 2: Technological Evolution - The evolution of Goal-Oriented Navigation can be categorized into three generations: 1. The first generation focuses on end-to-end methods using reinforcement and imitation learning, achieving breakthroughs in Point Navigation and closed-set image navigation tasks [5]. 2. The second generation employs modular methods that explicitly construct semantic maps, enhancing performance in zero-shot object navigation tasks [5]. 3. The third generation integrates large language models (LLMs) and visual language models (VLMs) to improve exploration strategies and open-vocabulary target matching accuracy [7][8]. Group 3: Challenges and Learning Path - The complexity of embodied navigation, particularly Goal-Oriented Navigation, necessitates knowledge from multiple fields, including natural language processing, computer vision, and reinforcement learning [10]. - The lack of systematic practical guidance and high-quality documentation in the Habitat ecosystem increases the difficulty for newcomers [10]. Group 4: Course Offering - A new course has been developed to address the challenges in learning Goal-Oriented Navigation, focusing on quick entry, building a research framework, and combining theory with practice [11][12][13]. - The course covers a comprehensive curriculum, including theoretical foundations, technical architectures, and practical applications in real-world scenarios [16][19][21][23].

Core Viewpoint - The article highlights the rapid advancements in embodied intelligence, particularly through the demonstration of Generalist AI's adaptive robots, showcasing their capabilities in complex physical tasks and the significant investment interest in this sector [4][6][11]. Group 1: Company Overview - Non-Xi Technology, founded in 2016, specializes in general-purpose intelligent robots and has received substantial investment from top-tier institutions, achieving unicorn status in 2022 [11][13]. - The company has developed a new category of "adaptive robots," which are designed to operate in unstructured environments, demonstrating high adaptability and precision in tasks [20][23]. Group 2: Technological Innovations - Non-Xi's self-developed Rizon "Dawn" robot features a seven-degree-of-freedom design, allowing it to perform complex operations that traditional industrial robots cannot [22][23]. - The company has created a comprehensive technology stack that includes hardware innovations and a restructured operating system, enabling easier deployment and programming of robots [26][27]. Group 3: Market Applications - Non-Xi's adaptive robots have been successfully applied in various industries, including automotive, electronics, and healthcare, showcasing their versatility in tasks such as assembly, surface treatment, and laboratory automation [36]. - The company has established partnerships with industry leaders to enhance its market presence and develop tailored solutions for specific sectors [32][34]. Group 4: Investment and Growth - Non-Xi recently completed a Series C funding round, raising significant capital to expand production, research, and ecosystem development [11][17]. - The company has achieved an average annual growth rate of over 200% for three consecutive years, indicating strong market demand and operational efficiency [34].

作者丨 机器之心 编辑丨 机器之心 点击下方 卡片 ，关注" 具身智能之心 "公众号 >> 点击进入→ 具身 智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 马斯克终于不「画饼」了！4.2美元坐特斯拉Robotaxi初体验：平稳但尚不成熟。 马斯克也在 X 上发文祝贺： 同时还透露，首批乘客将以「固定价格」4.20 美元搭乘。 马斯克兑现了承诺。 早在十年前，埃隆・马斯克就曾多次表示，特斯拉有能力推出无人驾驶服务，但后来却食言了。上周日，特斯拉终于在德克萨斯州奥斯汀正式启动了自动驾驶出 租车服务。 当然也可以付小费。 评论区的网友一片欢呼： 限定试运营，尚未全面开放 目前，特斯拉的 Robotaxi 服务 仅限受邀用户使用 ，并未向公众全面开放。首批试乘者主要为支持特斯拉的知名社交媒体博主和科技内容创作者，因此外界对其初 步评价的客观性仍持保留态度。至于该服务何时正式向公众开放，特斯拉尚未给出明确时间表。 此次小规模试运营共投入约 10 至 20 辆贴有 「Robotaxi」标识的 Model Y 车辆。而去年首次亮相、备受 ...