Sim2Real (Simulation to Reality) techniques have gained prominence in robotic manipulation and motion planning due to their ability to enhance success rates by enabling agents to test and evaluate various policies and trajectories. In this paper, we investigate the advantages of integrating Sim2Real into robotic frameworks. We introduce the Triple Regression Sim2Real framework, which constructs a real-time digital twin. This twin serves as a replica of reality to simulate and evaluate multiple plans before their execution in real-world scenarios. Our triple regression approach addresses the reality gap by: (1) mitigating projection errors between real and simulated camera perspectives through the first two regression models, and (2) detecting discrepancies in robot control using the third regression model. Experiments on 6-DoF grasp and manipulation tasks (where the gripper can approach from any direction) highlight the effectiveness of our framework. Remarkably, with only RGB input images, our method achieves state-of-the-art success rates. This research advances efficient robot training methods and sets the stage for rapid advancements in robotics and automation.

翻译：Sim2Real（仿真到现实）技术因其能够通过使智能体测试和评估各种策略与轨迹来提高成功率，在机器人操作与运动规划领域日益受到重视。本文研究了将Sim2Real集成到机器人框架中的优势。我们提出了三重回归Sim2Real框架，该框架构建了一个实时数字孪生体。此孪生体作为现实的副本，用于在真实场景执行前模拟和评估多种规划方案。我们的三重回归方法通过以下方式弥合现实差距：（1）利用前两个回归模型减少真实与仿真相机视角之间的投影误差，以及（2）利用第三个回归模型检测机器人控制中的差异。在6自由度抓取与操作任务（夹爪可从任意方向接近目标）上的实验凸显了我们框架的有效性。值得注意的是，仅使用RGB输入图像，我们的方法便达到了最先进的成功率。这项研究推进了高效的机器人训练方法，并为机器人与自动化领域的快速发展奠定了基础。