Simulation provides a cost-effective and flexible platform for data generation and policy learning to develop robotic systems. However, bridging the gap between simulation and real-world dynamics remains a significant challenge, especially in physical parameter identification. In this work, we introduce a real-to-sim-to-real engine that leverages the Gaussian Splat representations to build a differentiable engine, enabling object mass identification from real-world visual observations and robot control signals, while enabling grasping policy learning simultaneously. Through optimizing the mass of the manipulated object, our method automatically builds high-fidelity and physically plausible digital twins. Additionally, we propose a novel approach to train force-aware grasping policies from limited data by transferring feasible human demonstrations into simulated robot demonstrations. Through comprehensive experiments, we demonstrate that our engine achieves accurate and robust performance in mass identification across various object geometries and mass values. Those optimized mass values facilitate force-aware policy learning, achieving superior and high performance in object grasping, effectively reducing the sim-to-real gap.

翻译：仿真为机器人系统的数据生成与策略学习提供了一个经济高效且灵活的平台。然而，弥合仿真与真实世界动力学之间的差距仍然是一个重大挑战，尤其是在物理参数辨识方面。在本工作中，我们引入了一个真实-仿真-真实引擎，该引擎利用高斯泼溅表示构建了一个可微分引擎，使其能够从真实世界的视觉观测和机器人控制信号中识别物体质量，同时实现抓取策略学习。通过优化被操纵物体的质量，我们的方法能自动构建高保真且物理合理的数字孪生体。此外，我们提出了一种新颖的方法，通过将可行的人类演示迁移到仿真的机器人演示中，从而从有限的数据中训练具有力感知的抓取策略。通过全面的实验，我们证明了我们的引擎在各种物体几何形状和质量值下均能实现准确且鲁棒的质量识别性能。这些优化后的质量值促进了力感知策略的学习，在物体抓取任务中实现了卓越的高性能，有效缩小了仿真到真实的差距。