A High-Fidelity Digital Twin for Robotic Manipulation Based on 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2601.03200v1
• Date: Tue, 06 Jan 2026 17:29:10 GMT
• Title: A High-Fidelity Digital Twin for Robotic Manipulation Based on 3D Gaussian Splatting
• Abstract summary: We present a framework that constructs high-quality digital twins within minutes from sparse RGB inputs.<n>Our system employs 3D Gaussian Splatting (3DGS) for fast, photorealistic reconstruction as a unified scene representation.<n>In experiments with a Franka Emika Panda robot, we demonstrate that this enhanced geometric accuracy effectively supports robust manipulation in real-world trials.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developing high-fidelity, interactive digital twins is crucial for enabling closed-loop motion planning and reliable real-world robot execution, which are essential to advancing sim-to-real transfer. However, existing approaches often suffer from slow reconstruction, limited visual fidelity, and difficulties in converting photorealistic models into planning-ready collision geometry. We present a practical framework that constructs high-quality digital twins within minutes from sparse RGB inputs. Our system employs 3D Gaussian Splatting (3DGS) for fast, photorealistic reconstruction as a unified scene representation. We enhance 3DGS with visibility-aware semantic fusion for accurate 3D labelling and introduce an efficient, filter-based geometry conversion method to produce collision-ready models seamlessly integrated with a Unity-ROS2-MoveIt physics engine. In experiments with a Franka Emika Panda robot performing pick-and-place tasks, we demonstrate that this enhanced geometric accuracy effectively supports robust manipulation in real-world trials. These results demonstrate that 3DGS-based digital twins, enriched with semantic and geometric consistency, offer a fast, reliable, and scalable path from perception to manipulation in unstructured environments.

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