Benchmarking the Sim-to-Real Gap in Cloth Manipulation

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,...

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Vydáno v:IEEE robotics and automation letters Ročník 9; číslo 3; s. 2981 - 2988
Hlavní autoři: Blanco-Mulero, David, Barbany, Oriol, Alcan, Gokhan, Colome, Adria, Torras, Carme, Kyrki, Ville
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Benchmark testing
Benchmarks
bimanual manipulation
Cloth
Computing time
Data sets for robot learning
Datasets
deformable object manipulation
Deformation
Dynamics
Engines
Fabrics
Formability
Manipulator dynamics
Simulation
Simulators
Source code
Stability analysis
Task analysis
Trajectory
ISSN:2377-3766, 2377-3766
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Popis
Shrnutí: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 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 .
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2024.3360814