Accurate and safe grasping under dynamic and visually occluded conditions remains a core challenge in real-world robotic manipulation. We present SyncTwin, a digital twin framework that unifies fast 3D scene reconstruction and real-to-sim synchronization for robust and safety-aware grasping in such environments. In the offline stage, we employ VGGT to rapidly reconstruct object-level 3D assets from RGB images, forming a reusable geometry library for simulation. During execution, SyncTwin continuously synchronizes the digital twin by tracking real-world object states via point cloud segmentation updates and aligning them through colored-ICP registration. The updated twin enables motion planners to compute collision-free and dynamically feasible trajectories in simulation, which are safely executed on the real robot through a closed real-to-sim-to-real loop. Experiments in dynamic and occluded scenes show that SyncTwin improves grasp accuracy and motion safety, demonstrating the effectiveness of digital-twin synchronization for real-world robotic execution.

翻译：在动态与视觉遮挡条件下实现精确且安全的抓取，仍然是现实世界机器人操作中的核心挑战。本文提出SyncTwin，一种数字孪生框架，它集成了快速三维场景重建与虚实同步技术，旨在为此类环境提供鲁棒且具备安全感知的抓取能力。在离线阶段，我们采用VGGT从RGB图像快速重建物体级三维资产，构建用于仿真的可重用几何库。在执行过程中，SyncTwin通过点云分割更新跟踪现实世界物体状态，并借助彩色ICP配准进行对齐，从而持续同步数字孪生。更新后的孪生体使运动规划器能够在仿真中计算无碰撞且动态可行的轨迹，并通过虚实实闭环在真实机器人上安全执行。在动态与遮挡场景中的实验表明，SyncTwin提高了抓取精度与运动安全性，证明了数字孪生同步技术对于现实世界机器人执行的有效性。