dorsal/arxiv
View SchemaSyncTwin: Fast Digital Twin Construction and Synchronization for Safe Robotic Grasping
| Authors | Ruopeng Huang, Boyu Yang, Wenlong Gui, Jeremy Morgan, Erdem Biyik, Jiachen Li |
|---|---|
| Categories | |
| ArXiv ID | 2601.09920vv1 |
| URL | https://arxiv.org/abs/2601.09920 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
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.
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"date_created": "2026-02-17T05:53:23.754000Z",
"date_modified": "2026-02-17T05:53:23.754000Z",
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"record": {
"abstract": "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.",
"arxiv_id": "2601.09920",
"authors": [
"Ruopeng Huang",
"Boyu Yang",
"Wenlong Gui",
"Jeremy Morgan",
"Erdem Biyik",
"Jiachen Li"
],
"categories": [
"cs.RO"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "SyncTwin: Fast Digital Twin Construction and Synchronization for Safe Robotic Grasping",
"url": "https://arxiv.org/abs/2601.09920",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
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"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
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