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

Benchmarking the Sim-to-Real Gap in Cloth Manipulation