本文只做学术分享，如有侵权，联系删文 >> 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。 点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Dingkang Liang等 编辑丨具身智能之心 论文链接：https://arxiv.org/abs/2511.19430 在具身智能（Embodied AI）领域，任务规划（Task Planning）是让机器人理解人类指令并执行动作的关键。然而，现有的研究和数据集往往将任务过度简化，假设 机器人只能串行（Sequential）地完成子任务，如图1(a)。 例如，面对指令："把微波炉打开热饭（需要35分钟），然后把水槽洗干净（需要 20 分钟）"。 这种差距的核心在于：现有机器人缺乏运筹学（Operations Research，OR）知识，无法识别哪些任务可以"并行（Parallelizable）"，哪些必须"独占注意力（Non- parallelizable）"。同时，机器人不仅要规划时间，还得在复杂的 3D 场景中精准找到物体的位置（3D Grou ...

本文只做学术分享，如有侵权，联系删文 >> 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。 论文链接：https://arxiv.org/abs/2511.19430 代码链接：https://github.com/H-EmbodVis/GRANT 点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Dingkang Liang等 编辑丨具身智能之心 在做饭时，人们通常会在微波炉加热食物的同时去清洗水槽，而不是呆板地盯着微波炉倒计时。然而，目前的具身智能机器人却往往只能"一根筋"地按顺序做完一 件事，再做下一件。 近日，华中科技大学（白翔团队）联合小米的论文《Cook and Clean Together: Teaching Embodied Agents for Parallel Task Execution》被 AAAI 2026 录用为口头报告 （Oral Presentation）。该工作首次将运筹学（Operations Research, OR）知识引入 3D 具身任务规划中。研究团队提出 ...

学界应回到工厂现场，不只是抽象地定义什么是"中国特色管理"，更应在工厂实践中，总结中国企业在应对复杂现实中的组织智慧与制度创造力。 工厂——管理学的原点 工厂，这一曾被视作"铁与火的熔炉"的场所，不仅塑造了人类文明的工业基础，更孕育了现代管理理论的基石。从轧钢车间的蒸汽轰鸣，到无尘车间里算 法的低语，工厂始终扮演着连接技术进步与组织演化的枢纽角色，是管理学的原点、管理思想的策源地。 19世纪末，科学管理在宾夕法尼亚的钢铁厂中萌芽；20世纪初，流水线革命在底特律的汽车工厂引爆；20世纪中叶的人际关系理论则在芝加哥的霍桑工厂 引发深远影响；而后，丰田的生产系统以精益哲学震撼世界，奠定了高效率与高柔性并存的新范式。直至今日，AI算法、数字孪生、工业机器人纷纷涌 入生产车间，工厂依然是各类管理思想与技术的"试验场"与"演化场"。这一历史脉络昭示出一个重要事实：管理理论从来不是空中楼阁，它是组织在面对 具体问题时的系统性回应与机制化反思。面对复杂劳动、资源稀缺、协同困难等现实难题，工厂在不断摸索中逐步催生了从效率优化到人本激励、从流程 设计到战略调度的一系列管理创新。 进入21世纪，随着大数据、人工智能、边缘计算等技术 ...

Core Viewpoint - The introduction of the TAOIX system's flight scheduling module by China Southern Airlines aims to enhance the efficiency and safety of operations during the peak summer travel season through comprehensive digitalization [1][2]. Group 1: Digital Transformation in Flight Scheduling - The new flight scheduling module utilizes operations research and artificial intelligence to improve task distribution and personnel matching, addressing inefficiencies in traditional scheduling methods [2]. - The module covers various maintenance scenarios, creating a closed-loop management system that enhances scheduling efficiency by reducing reliance on manual notifications [2][3]. - The intelligent scheduling algorithm and real-time monitoring capabilities allow for precise task and personnel matching, significantly improving the scientific and efficient nature of scheduling [2][3]. Group 2: Real-time Data Integration and Response - The module introduces a digital monitoring mechanism that connects with multiple data platforms, enabling a dynamic response system to flight changes [3]. - Automatic alert functions provide timely notifications to staff and management, facilitating quick task handling and improving overall operational efficiency [3]. - The system supports real-time tracking of key performance indicators, aiding in decision-making for scheduling strategies and resource allocation [3]. Group 3: Nationwide Implementation and Future Directions - The flight scheduling module has been successfully piloted in several bases, with significant improvements noted in monitoring and task management [4]. - The module has been rolled out to 17 maintenance units, expanding the coverage of digital scheduling [4]. - Future initiatives will focus on standardizing processes, digitizing management, and enhancing decision-making capabilities to further advance the digital transformation in maintenance operations [4].

Group 1 - The essence of digital supply chains lies in building capabilities for end-to-end integration and innovation, rather than merely applying specific technologies or tasks [2] - Three key suggestions for AI application in supply chains include establishing a foundation for data and process integration, avoiding an exclusive focus on large models, and combining AI with operations research for better decision-making [2][4] - The evolution of China's supply chain has gone through four stages, from execution-focused to strategic integration, user-centric digital connections, and now to building supply chain ecosystems [3] Group 2 - Current trends in supply chain management emphasize rapid response, resilience, reconstruction, and green low-carbon initiatives, all of which are fundamentally supported by digitalization [4] - Future supply chain capabilities will depend on deeper data integration and analysis, merging operations research models with AI technology to enhance decision-making processes [5] - Successful logistics companies have transitioned from human management to system management, focusing on optimizing data usage to improve employee experience and customer value [5] Group 3 - The diversification of channels leads to order fragmentation, with many brands unaware of the full fulfillment path, resulting in high costs and inefficiencies [6] - AI and big data analytics play a crucial role in optimizing supply chain operations, such as determining warehouse locations and balancing inventory levels [6] - Multi-point intelligent supply chains utilize AI for efficient route planning and inventory management, significantly improving operational metrics like delivery efficiency and stock availability [7]