Realistic physics engines play a crucial role for learning to manipulate deformable objects such as garments in simulation. By doing so, researchers can circumvent challenges such as sensing the deformation of the object in the real-world. In spite of the extensive use of simulations for this task, few works have evaluated the reality gap between deformable object simulators and real-world data. We present a benchmark dataset to evaluate the sim-to-real gap in cloth manipulation. The dataset is collected by performing a dynamic cloth manipulation task involving contact with a rigid table. We use the dataset to evaluate the reality gap, computational time, and simulation stability of four popular deformable object simulators: MuJoCo, Bullet, Flex, and SOFA. Additionally, we discuss the benefits and drawbacks of each simulator. The benchmark dataset is open-source. Supplementary material, videos, and code, can be found at https://sites.google.com/view/cloth-sim2real-benchmark.

翻译：真实物理引擎在模拟学习操控衣物等可变形物体中扮演关键角色。通过这种方式，研究人员可以规避在现实世界中感知物体变形等挑战。尽管此任务中仿真已被广泛使用，但很少有研究评估可变形物体模拟器与现实数据之间的现实差距。我们提出一个基准数据集，用于评估布料操控中的仿真到现实差距。该数据集通过执行涉及与刚性桌面接触的动态布料操控任务收集。我们利用该数据集评估了四种主流可变形物体模拟器（MuJoCo、Bullet、Flex和SOFA）的现实差距、计算时间和仿真稳定性。此外，我们讨论了每种模拟器的优缺点。该基准数据集为开源。补充材料、视频和代码可在 https://sites.google.com/view/cloth-sim2real-benchmark 获取。