D-REX：用于学习灵巧抓取的可微现实到仿真到现实引擎
D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping
AI总结：
D-REX通过可微引擎实现现实到仿真到现实的抓取学习，利用高斯点表示自动构建数字双胞胎并优化质量识别，从而提升抓取策略的力感知性能。
摘要：
仿真提供了一个成本效益高且灵活的数据生成和策略学习平台，用于开发机器人系统。然而，弥合仿真与现实世界动态之间的差距仍然是一个重大挑战，尤其是在物理参数识别方面。在本工作中，我们介绍了一个现实到仿真到现实的引擎，利用高斯点表示来构建可微引擎，使从现实世界的视觉观察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。通过优化被操作物体的质量，我们的方法自动构建高保真且物理上合理的数字双胞胎。此外，我们提出了一种新的方法，从有限的数据中训练力感知的抓取策略，通过将可行的人类示范转换为仿真的机器人示范。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上实现了准确且稳健的质量识别性能。这些优化的质量值促进了力感知策略的学习，实现了在物体抓取中的优越和高性能，有效缩小了仿真到现实的差距。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
