Learning Efficient Robotic Garment Manipulation with Standardization

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&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% better coverage, 5.2% higher IoU, and a 0.14 decrease in KD (7.09% relative reduction). Real-world folding tasks further demonstrate that standardization simplifies the folding process. Project page: https://hellohaia.github.io/APS/

Robots often struggle to work with clothes because garments are soft, crumpled, and tricky to handle. We developed a method that helps robots not only unfold clothes quickly, but also lay them out neatly in a consistent way. It combines simple actions like tossing and placing to make the garment flat and aligned, which makes later tasks like folding or packing easier. We also taught the robot what a “well-arranged” piece of clothing should look like using new training method. Our approach showed better performance than earlier techniques in both simulated and real-world tests, and it could make robotic laundry systems more useful at home and in industry.