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 this https URL

通过评估可变形物体模拟器与真实数据之间的现实差距，我们提出了一个用于评估布料操作中模拟与真实之间差距的基准数据集。我们使用这个数据集评估了四个流行的可变形物体模拟器：MuJoCo，Bullet，Flex 和 SOFA 的现实差距、计算时间和模拟稳定性，并讨论了每个模拟器的优缺点。这个基准数据集是开源的，补充材料、视频和代码可以在给出的网址找到。