ClothMate: Leveraging Grasp-Fling Consistency for Generalizable and Data-Efficient Garment Flattening

Abstract

We present ClothMate, a general framework for flattening garments of various categories from arbitrary configurations. Prior end-to-end methods are often limited in data efficiency. To address this, ClothMate introduces an intriguing observation: in garment flattening, grasping the same point pair typically results in a consistent fling response, regardless of the current state of the garment. Building on this insight, we adopt a two-stage paradigm: first learning point- wise fling responses under a canonicalized and aligned configuration, then generalizing to arbitrary states via point-to-point projection. This explicit decoupling of state estimation and action prediction mitigates challenges arising from infinite-DOF dynamics and severe self-occlusion, enabling the model to better capture common interaction patterns, thus improving data efficiency to support a broader range of garment categories. Additionally, we propose a novel static pick-and-stretch dual-arm flattening strategy that refines the outcome by heuristically stretching around adaptively selected key points. Compared to the state of the art, ClothMate is trained and evaluated simultaneously on five garment categories, using only 15% of the total data that baselines would require for separate training on all five categories, while achieving higher coverage (91.5% vs. 85.0%) and fewer steps (4.7 vs. 7.0). The code, dataset and trained models are available here.