FabricBench: A Standardized Policy Benchmark for Bimanual Fabric Manipulation with a Contact-Driven Deformable Simulator

Fabric manipulation challenges robot policies with changing geometry, self-occlusion, and contact, yet standardized evaluation remains focused on rigid objects. We introduce FabricBench, a benchmark for bimanual fabric manipulation with six tasks spanning garments, silk, and bags, 10,000 simulation-ready assets, and controlled tests of configuration and appearance generalization. Built on an open-source solver in Newton, our simulator combines redesigned cloth–rigid contact with bidirectional force coupling, enabling grasping, release, and load transfer without artificial attachment. It succeeds in all 45 grasp–hold–release validation trials, compared with none for the tested baseline configurations. Evaluating six representative policies, including vision-language-action models and world action models, reveals substantial remaining challenges: the best policy achieves 77% average success, while no policy exceeds 36% on long-horizon T-shirt folding. Under the most deformed initial T-shirt configurations, success falls to 6–14% across policies, with larger drops than under the evaluated appearance shifts. Zero-shot deployment of π₀.₅ on a real robot largely preserves task difficulty ordering across six tasks (Spearman ρ = 0.94). These results establish FabricBench as a reproducible testbed for measuring fabric-manipulation capabilities and exposing limitations in configuration generalization.