Core Insights - The article discusses the evolution of Unmanned Aerial Vehicles (UAVs) into Agentic UAVs, which are characterized by autonomous reasoning, multimodal perception, and reflective control, marking a significant shift from traditional automation platforms [5][6][11]. Research Background - The motivation for this research stems from the rapid development of UAVs from remote-controlled platforms to complex autonomous agents, driven by advancements in artificial intelligence (AI) [6][7]. - The increasing demand for autonomy, adaptability, and interpretability in UAV operations across various sectors such as agriculture, logistics, environmental monitoring, and public safety is highlighted [6][7]. Definition and Architecture of Agentic UAVs - Agentic UAVs are defined as a new class of autonomous aerial systems with cognitive capabilities, situational adaptability, and goal-directed behavior, contrasting with traditional UAVs that operate based on predefined instructions [11][12]. - The architecture of Agentic UAVs consists of four core layers: perception, cognition, control, and communication, enabling autonomous sensing, reasoning, action, and interaction [12][13]. Enabling Technologies - Key technologies enabling the development of Agentic UAVs include: - **Perception Layer**: Utilizes a suite of sensors (RGB cameras, LiDAR, thermal sensors) for real-time semantic understanding of the environment [13][14]. - **Cognition Layer**: Acts as the decision-making core, employing techniques like reinforcement learning and probabilistic modeling for adaptive control strategies [13][14]. - **Control Layer**: Converts planned actions into specific flight trajectories and commands [13][14]. - **Communication Layer**: Facilitates data exchange and task coordination among UAVs and other systems [13][14]. Applications of Agentic UAVs - **Precision Agriculture**: Agentic UAVs are transforming precision agriculture by autonomously identifying crop health issues and optimizing pesticide application through real-time data analysis [17][18]. - **Disaster Response and Search and Rescue**: These UAVs excel in dynamic environments, providing real-time adaptability and autonomous task reconfiguration during disaster scenarios [20][21]. - **Environmental Monitoring**: Agentic UAVs serve as intelligent, mobile environmental sentinels, capable of monitoring rapidly changing ecosystems with high spatial and temporal resolution [22][23]. - **Urban Infrastructure Inspection**: They offer a transformative approach to infrastructure inspections, enabling real-time damage detection and adaptive task planning [24]. - **Logistics and Smart Delivery**: Agentic UAVs are emerging as intelligent aerial couriers, capable of executing complex delivery tasks with minimal supervision [25][26]. Challenges and Limitations - Despite the transformative potential of Agentic UAVs, their widespread application faces challenges related to technical constraints, regulatory hurdles, and cognitive dimensions [43].

Group 1 - The article emphasizes the importance of choosing a reliable programming training institution, particularly in the context of limited local options in Tongling [1][3] - It suggests that courses should be practical and cover mainstream technologies like Python, Java, and front-end development, with a focus on real project experience [3][4] - The quality of instructors is highlighted, recommending prospective students to attend trial classes to assess teaching effectiveness [3][4] Group 2 - The learning environment is crucial; institutions with mechanisms for accountability, such as project reviews and attendance tracking, are preferred to prevent students from losing motivation [3][4] - Online learning is presented as a viable option, especially for students in non-first-tier cities, with a success story of a student transitioning to a tech job after completing an online Java course [4][5] - The article mentions a specific online education provider, Wangshidai Education, which has a strong reputation, high employment rates, and offers comprehensive Java courses with practical projects [4][5]

暑期消费热力十足，"后高考经济""毕业经济"热度居高不下，电子产品、生活电器消费迅速升温，折射 出年轻人消费新趋势。消费需求集中释放带动线下门店客流攀升，苏宁易购门店凭借品质化产品、场景 化体验、定制化服务承接暑期家电3C消费热潮。 苏宁易购相关负责人介绍，高考后至放榜期间，以电子产品为代表的"奖励式消费"迎来高峰。6月10日 至6月25日，手机、电脑、智能手表手环销售同比增长43%、69%、234%。不少家长为孩子备上"数码全 家桶"，手机、电脑、平板等数码套购订单增长近一倍。在北京中塔Suning Max店，胡同学在父母的陪 同下购入包括手机、电脑、平板、耳机在内的"数码四大件"，"查完分就来了，和爸妈一起把开学装备 选一下，店里品牌型号比较全，整套买也有折扣，很方便。" 一批莘莘学子即将迈入大学，另一批年轻人离开校园走入社会，开启新生活，苏宁易购门店为不同阶段 的年轻人提供生活方式新提案。在南京徐庄广场Suning Fun，彭同学即将和好友开启合租生活，"虽然 是租房，但生活是自己的，我们买了投影仪，不仅提升幸福感，未来搬家也方便"，在门店的露营装备 区，彭同学准备一并购入移动电源，"周末出去玩用得上" ...

Core Viewpoint - The development of embodied intelligence technology is a leading trend in the AI and robotics sector, involving advanced techniques such as large language models (LLM), visual multimodal models (VLM), reinforcement learning, deep reinforcement learning, and imitation learning [1]. Group 1: Embodied Intelligence Technology - Embodied intelligence technology encompasses various cutting-edge techniques, including LLM, VLM, reinforcement learning, deep reinforcement learning, and imitation learning [1]. - The evolution of humanoid robots has progressed from model-based control algorithms to dynamic model control and optimal control algorithms, and currently to simulation combined with reinforcement learning [1]. - The most frequently mentioned concepts in humanoid robotics companies are imitation learning and reinforcement learning, primarily researched by academic and leading tech company teams [1]. Group 2: Academic Contributions - UC Berkeley and Stanford University are leading institutions in the AI and robotics research field, with notable alumni contributing to the embodied intelligence sector [2]. - Four prominent figures from UC Berkeley, known as the "Four Returnees," have transitioned from Tsinghua University to UC Berkeley and then to entrepreneurial ventures in embodied intelligence [2]. Group 3: Notable Individuals in the Field - Wang He and Lu Ce Wu are key representatives of individuals who graduated from Stanford University and are now involved in the embodied intelligence startup scene in China [3]. - Wang He, a 2021 PhD graduate from Stanford, is now an assistant professor at Peking University and the founder of a leading humanoid robotics startup [3]. - Lu Ce Wu, a postdoctoral researcher at Stanford, is a co-founder and chief scientist of a unicorn collaborative robotics company and a founder of an embodied intelligence startup [3]. Group 4: Global Talent Pool - The majority of the top 50 Chinese individuals in the embodied intelligence field have educational backgrounds from prestigious institutions such as UC Berkeley, Stanford, MIT, and CMU, often under the mentorship of industry leaders [4]. - A detailed mapping of the top 50 Chinese talents in the field includes their educational history, research directions, and current positions in leading tech companies or startups [5].

近期北京邮电大学方斌教授团队联合清华大学、 意大利比萨圣安娜大学、英国伦敦国王学院和德国汉堡大学 发布了基于掌状视触觉传感器的柔性接触仿真与操作学习，为基于视触觉传感器的柔性操作提供了新的思路。 相关工作发表在机器人、自动化领域 JCR Q1 期刊 IEEE Transactions on Automation Science and Engineering 。 研究背景： 可变形物体操控是机器人领域一个经典且极具挑战性的任务。相较于刚性物体，可变性物体具复杂的变形特性 （包括弹性变形、塑性变形和弹塑性变形），大量的自由度 (DOF) 需要复杂的建模方法，这使该问题更加复 杂。同时，可变形物体广泛存在于医院、工业和家庭环境中。因此，可变形物体操控在机器人技术发展中发挥 着至关重要的作用。 为此，本文开发了一款可变形物体与基于视觉的触觉传感器之间的软接触模拟器，该模拟器能够模拟视触觉传 感器与弹性、塑性以及弹塑性物体之间的接触变形。在此模拟器的基础上，本文提出了基于视触觉传感器的可 变形物体操控基准，包括可迁移的观测值、任务和专家演示系统。最后，本文搭建了相应的实验平台，完成了 相关任务的 Sim-to-rea ...

过去，灵巧手更多是实验室中的符号——高自由度、仿生结构、极高成本；而如今，伴随软硬协同能力的提升、控制算法的不断演进、触觉与多模态感知 的加速融合，灵巧手正逐步从科研走向应用的临界点。一方面，它是工业自动化对"异形抓取""多任务执行"能力的新需求延伸；另一方面，它也是服务机 器人在家庭、医疗、养老等场景中迈向"真实可用"的关键一环。 当AI从云端走向实体，具身智能正逐渐成为通往下一代通用人工智能的关键路径。在这一演进过程中，灵巧手作为"通用机器人"实现复杂操作与自然交互 的核心执行器，正迎来前所未有的技术突破与商业想象空间。 值得注意的是，这一领域正快速演变为全球技术博弈与资本布局的热点。从Shadow Robot与DeepMind合作攻克多任务抓取，到中国本土初创企业灵心巧 手凭借超高自由度结构在仿生手赛道突围，一批聚焦结构创新、感知控制一体化的新兴力量，正在不断刷新我们对"灵巧"这一词的理解。 我们希望通过本报告，为关注具身智能、机器人末端执行器、智能制造升级的产业人士与投资机构，提供一份具备前瞻视角与产业落点的深度参考。本篇 报告将围绕以下三大维度系统展开： 产业定义与技术演进 应用场景与商业趋势 竞争 ...

[Table_ReportInfo] 2025 年 06 月 30 日 证券研究报告•金融工程报告 机器学习金融工程月报 机器学习因子选股月报（2025 年 7 月） 摘要 西南证券研究院 [Table_Author] 分析师：郑琳琳 执业证号：S1250522110001 邮箱：zhengll@swsc.com.cn 联系人：祝晨宇 邮箱：zhcy@swsc.com.cn 相关研究 请务必阅读正文后的重要声明部分 [Table_Summary] GAN_GRU因子：基于前期报告《量价时序特征挖掘模型在深度学习因子中的 应用》（2024年 7月 11日）中提到的 GAN_GRU 模型，定期更新 GAN_GRU 因子自 2024年以来表现情况。其中 GAN_GRU因子为利用生成式对抗神经网 络 GAN 模型进行量价时序特征处理后再利用 GRU 模型进行时序特征编码得 到的选股因子。 表现跟踪：自 2019 年 1 月至 2025 年 6 月，月频调仓的基础上，GAN_GRU 因子在全 A 范围内 IC均值 11.54%，多头组合年化超额收益率 24.95%；截至 2025 年 6 月 27 日，GAN_GRU ...

Group 1 - The core viewpoint of the article emphasizes that technological innovation, particularly through the introduction of machine learning, has become a key driver for the development of companies in the industrial sector [1][2]. - Shenzhen Xiongyu Rubber Hardware Products Co., Ltd. has successfully optimized its silicone rubber production process by implementing machine learning technology, significantly improving production efficiency and product quality [1][3]. Group 2 - The company has developed an intelligent production system based on machine learning, which collects and analyzes large amounts of production data to achieve real-time optimization and precise control of production parameters [3][4]. - The introduction of machine learning has led to a 15% increase in product qualification rates and a substantial reduction in defect rates, enhancing product quality consistency [3][4]. - Production efficiency has improved by approximately 21%, and production cycles have been shortened by about 14% due to optimized production processes and reduced downtime [4]. Group 3 - Industry experts have praised the company's innovative achievements, highlighting the integration of machine learning in traditional manufacturing as a model for enhancing core competitiveness and providing valuable insights for the industry [5]. - The company plans to continue increasing research and development investments to explore more advanced technologies in silicone rubber production, aiming for further breakthroughs and contributions to high-quality industry development [5].

Group 1 - The meeting emphasized the importance of implementing the spirit of the Central Eight Regulations and enhancing the political stance of party members [1][2] - Focus will be on addressing issues such as increasing burdens on grassroots, improper dining, and the misuse of funds, with a strong commitment to rectification [2] - The need for a systematic approach to ensure the effectiveness of learning and education initiatives was highlighted, aiming for long-term institutional mechanisms [2] Group 2 - The meeting was chaired by Fang Hongwei, who stressed the importance of strict adherence to work and integrity disciplines among supervisory teams [2] - Coordination among municipal work teams is essential for timely communication of central and provincial requirements [2][3] - The meeting included participation from various city-level officials and supervisory group leaders to ensure comprehensive engagement in the educational efforts [2]

在近20年AI发展的路线上，我们正站在⼀个前所未有的转折点。从早期的符号推理到深度学习的突破，再到 如今⼤语⾔模型的惊艳表现， AI 技术的每⼀次⻜跃都在重新定义着⼈类与机器的关系。⽽如今，具身智能正 在全面崛起。 想象⼀下这样的场景：⼀个机器⼈不仅能够理解你的语⾔指令，还能在复杂的现实环境中灵活移动，精确操作 各种物体，甚⾄在⾯对突发情况时做出智能决策。这不再是科幻电影中的幻想，⽽是正在快速成为现实的技术 ⾰命。从Tesla的Optimus⼈形机器⼈到Boston Dynamics的Atlas，从OpenAI的机械⼿到Google的RT-X项⽬，全 球顶尖的科技公司都在竞相布局这⼀颠覆性领域。具身智能的核⼼理念在于让AI系统不仅拥有"⼤脑"，更要拥 有能够感知和改变物理世界的"身体"。这种AI不再局限于虚拟的数字空间，⽽是能够真正理解物理定律、掌握 运动技能、适应复杂环境。它们可以在⼯⼚中进⾏精密装配，在医院⾥协助⼿术操作，在家庭中提供贴⼼服 务，在危险环境中执⾏救援任务。这种技术的潜在影响⼒是⾰命性的：它将彻底改变制造业、服务业、医疗健 康、太空探索等⼏乎所有⾏业。 从顶级会议ICRA 、IROS到Neu ...