PathFormer: A Transformer with 3D Grid Constraints for Digital Twin Robot-Arm Trajectory Generation
Department of Computer Science, University of Missouri–Kansas City (UMKC)（密苏里大学哥伦比亚分校计算机科学系）；Department of Computer Science, California State University, Fullerton（加州州立大学富尔顿分校计算机科学系）
机器人臂需要精确且任务感知的轨迹规划，然而忽略运动结构的序列模型往往会产生无效或低效的执行。我们提出了一种基于路径的Transformer，它使用3网格（何处/什么/何时）表示和约束掩码解码来编码机器人运动，在推理任务图和动作顺序的同时，强制晶格相邻移动和工作空间边界。该模型在53,755条轨迹（80%训练/20%验证）上进行训练，与地面实况高度一致——逐步准确率89.44%，精确率93.32%，召回率89.44%，F1分数90.40%——并且99.99%的路径在构造上合法。在带有深度相机数字孪生的xArm Lite 6上编译为电机原语后，在受控测试中实现了高达97.5%的到达成功率和92.5%的抓取成功率，在杂乱场景中的60个语言指定任务中实现了86.7%的端到端成功率，通过局部重新定位吸收滑动和遮挡，而无需全局重新规划。这些结果表明，路径结构化表示使Transformer能够生成准确、可靠且可解释的机器人轨迹，弥合了基于图的规划和基于序列的学习之间的差距，为通用操作和模拟到现实迁移提供了实用基础。
英文摘要：
Robotic arms require precise, task-aware trajectory planning, yet sequence models that ignore motion structure often yield invalid or inefficient executions. We present a Path-based Transformer that encodes robot motion with a 3-grid (where/what/when) representation and constraint-masked decoding, enforcing lattice-adjacent moves and workspace bounds while reasoning over task graphs and action order. Trained on 53,755 trajectories (80% train / 20% validation), the model aligns closely with ground truth -- 89.44% stepwise accuracy, 93.32% precision, 89.44% recall, and 90.40% F1 -- with 99.99% of paths legal by construction. Compiled to motor primitives on an xArm Lite 6 with a depth-camera digital twin, it attains up to 97.5% reach and 92.5% pick success in controlled tests, and 86.7% end-to-end success across 60 language-specified tasks in cluttered scenes, absorbing slips and occlusions via local re-grounding without global re-planning. These results show that path-structured representations enable Transformers to generate accurate, reliable, and interpretable robot trajectories, bridging graph-based planning and sequence-based learning and providing a practical foundation for general-purpose manipulation and sim-to-real transfer.
RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins (early version)
The University of Hong Kong（香港大学）；AgileX Robotics（极目机器人）；Shanghai AI Laboratory（上海人工智能实验室）；Shenzhen University（深圳大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Goal-oriented Semantic Communication for Robot Arm Reconstruction in Digital Twin: Feature and Temporal Selections
Department of Engineering, King’s College London（伦敦国王学院工程系）；ZhejiangLab（浙江实验室）；Bristol Research and Innovation Laboratory, Toshiba Europe Ltd.（巴斯德研究与创新实验室，东芝欧洲有限公司）
Building Information Models to Robot-Ready Site Digital Twins (BIM2RDT): An Agentic AI Safety-First Framework
Department of Civil, Construction, and Environmental Engineering, San Diego State University, San Diego, CA, United States（土木、建设与环境工程系，圣地亚哥州立大学）；Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, CA, United States（电气与计算机工程系，加州大学圣地亚哥分校）；Department of Mechanical and Aerospace Engineering, University of California, San Diego, San Diego, CA, United States（机械与航空航天工程系，加州大学圣地亚哥分校）；Department of Computer Science, San Diego State University, San Diego, CA, United States（计算机科学系，圣地亚哥州立大学）
AI-enabled Cyber-Physical In-Orbit Factory -- AI approaches based on digital twin technology for robotic small satellite production
Zentrum für Telematik e.V.（电信中心协会）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（德国航空航天中心机器人与机电一体化研究所）
IMMERTWIN: A Mixed Reality Framework for Enhanced Robotic Arm Teleoperation
School of Computing Science, University of Glasgow（格拉斯哥大学计算科学学院）；ICPS Research Center, National Institute of Advanced Industrial Science and Technology (AIST)（日本国立先进工业科学技术研究院ICPS研究中心）
Digital Twin Driven Textile Classification and Foreign Object Recognition in Automated Sorting Systems
Institute of Smart Systems Technologies（智能系统技术研究所）；University of Klagenfurt（克雷格弗特大学）；AAU SAL USE Laboratory（AAU SAL USE实验室）；Silicon Austria Labs（硅 Austria 实验室）
Digital Twin Synchronization: Bridging the Sim-RL Agent to a Real-Time Robotic Additive Manufacturing Control
Department of Mechanical Engineering, University of Louisiana at Lafayette（路易斯安那大学拉斐特分校机械工程系）；Computer Science & Information Systems Department, Bradley University（布雷纳德大学计算机科学与信息系统系）
Hybrid Task and Motion Planning with Reactive Collision Handling for Multi-Robot Disassembly of Complex Products: Application to EV Batteries
Extreme Robotics Laboratory, School of Metallurgy and Materials, University of Birmingham（极端机器人实验室，冶金与材料学院，伯明翰大学）；Department of Applied Artificial Intelligence and Robotics, Aston University（应用人工智能与机器人学院，阿斯顿大学）
