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 realworld. 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 as well as a quasi-static 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：逼真的物理引擎对于在仿真中学习操控服装等可变形物体至关重要。通过这种方式，研究人员可规避现实世界中感知物体变形等挑战。尽管仿真在此任务中得到广泛应用，但很少有工作评估可变形物体仿真器与现实数据之间的现实差距。我们提出了一个基准数据集，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和准静态布料操控任务收集而来。我们利用该数据集评估了四种主流可变形物体仿真器（MuJoCo、Bullet、Flex 和 SOFA）的现实差距、计算时间和仿真稳定性。此外，我们讨论了各仿真器的优缺点。该基准数据集为开源资源。补充材料、视频及代码可访问 https://sites.google.com/view/cloth-sim2real-benchmark 获取。