布マニピュレーションにおけるsim-to-realギャップのベンチマーク
Benchmarking the Sim-to-Real Gap in Cloth Manipulation
布の操作タスクを対象に、4つの物理シミュレータ(MuJoCo, Bullet, Flex, SOFA)と実世界データのギャップを評価するベンチマークデータセットを構築し、再現性・計算時間・安定性を比較した。
著者: David Blanco-Mulero, Oriol Barbany, Gokhan Alcan, Adrià Colomé, Carme Torras, Ville Kyrki
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
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.
