Learning Efficient Robotic Garment Manipulation with Standardization
Journal:
arXiv
Published Date:
Jun 28, 2025
Abstract
Garment manipulation is a significant challenge for robots due to the complex
dynamics and potential self-occlusion of garments. Most existing methods of
efficient garment unfolding overlook the crucial role of standardization of
flattened garments, which could significantly simplify downstream tasks like
folding, ironing, and packing. This paper presents APS-Net, a novel approach to
garment manipulation that combines unfolding and standardization in a unified
framework. APS-Net employs a dual-arm, multi-primitive policy with dynamic
fling to quickly unfold crumpled garments and pick-and-place (p and p) for
precise alignment. The purpose of garment standardization during unfolding
involves not only maximizing surface coverage but also aligning the garment's
shape and orientation to predefined requirements. To guide effective robot
learning, we introduce a novel factorized reward function for standardization,
which incorporates garment coverage (Cov), keypoint distance (KD), and
intersection-over-union (IoU) metrics. Additionally, we introduce a spatial
action mask and an Action Optimized Module to improve unfolding efficiency by
selecting actions and operation points effectively. In simulation, APS-Net
outperforms state-of-the-art methods for long sleeves, achieving 3.9 percent
better coverage, 5.2 percent higher IoU, and a 0.14 decrease in KD (7.09
percent relative reduction). Real-world folding tasks further demonstrate that
standardization simplifies the folding process. Project page: see
https://hellohaia.github.io/APS/
