Sim-to-Real refers to the process of transferring policies learned in simulation to the real world, which is crucial for achieving practical robotics applications. However, recent Sim2real methods either rely on a large amount of augmented data or large learning models, which is inefficient for specific tasks. In recent years, radiance field-based reconstruction methods, especially the emergence of 3D Gaussian Splatting, making it possible to reproduce realistic real-world scenarios. To this end, we propose a novel real-to-sim-to-real reinforcement learning framework, RL-GSBridge, which introduces a mesh-based 3D Gaussian Splatting method to realize zero-shot sim-to-real transfer for vision-based deep reinforcement learning. We improve the mesh-based 3D GS modeling method by using soft binding constraints, enhancing the rendering quality of mesh models. We then employ a GS editing approach to synchronize rendering with the physics simulator, reflecting the interactions of the physical robot more accurately. Through a series of sim-to-real robotic arm experiments, including grasping and pick-and-place tasks, we demonstrate that RL-GSBridge maintains a satisfactory success rate in real-world task completion during sim-to-real transfer. Furthermore, a series of rendering metrics and visualization results indicate that our proposed mesh-based 3D Gaussian reduces artifacts in unstructured objects, demonstrating more realistic rendering performance.

翻译：仿真到现实（Sim-to-Real）指将在仿真环境中学习到的策略迁移至真实世界的过程，这对于实现实用的机器人应用至关重要。然而，现有的Sim2real方法要么依赖大量增强数据，要么需要大型学习模型，对于特定任务而言效率低下。近年来，基于辐射场的重建方法，特别是3D高斯溅射的出现，使得复现逼真的真实场景成为可能。为此，我们提出了一种新颖的真实-仿真-真实强化学习框架RL-GSBridge，该方法引入了一种基于网格的3D高斯溅射方法，以实现基于视觉的深度强化学习的零样本仿真到现实迁移。我们通过使用软绑定约束改进了基于网格的3D GS建模方法，从而提升了网格模型的渲染质量。随后，我们采用一种GS编辑方法，使渲染与物理仿真器同步，更准确地反映物理机器人的交互。通过一系列仿真到现实的机械臂实验，包括抓取和取放任务，我们证明了RL-GSBridge在仿真到现实迁移过程中，在真实世界任务完成方面保持了令人满意的成功率。此外，一系列渲染指标和可视化结果表明，我们提出的基于网格的3D高斯方法减少了非结构化物体的伪影，展现了更逼真的渲染性能。