Efficient Planar Fabric Repositioning: Deformation-Aware RRT* for Non-Prehensile Fabric Manipulation

Abstract

Fabrics present significant challenges to robotic manipulation due to their complex dynamics and infinite degrees of freedom. This letter proposes a non-prehensile approach to aligning a fabric cut piece to a specified target pose, which is a common step for many garment manufacturing tasks. Compared to widely explored prehensile manipulation that grasps and lifts the fabric, the proposed approach uses pushing actions and allows for efficient fabric repositioning with a simpler end-effector. To handle the possible deformation caused by the pushing actions, we introduce the Deformation-aware Rapidly-exploring Random Tree Star (D-RRT*) algorithm that strategically plans the contact poses and actions to slide the fabric to desired configurations by leveraging the friction. Our D-RRT* algorithm enhances fabric manipulation by incorporating deformation-aware action sampling into the path planning process, enabling accurate predictions of non-prehensile actions and efficient navigation through the fabric's configuration space. Extensive simulations and real-world experiments on repositioning fabrics of various shapes and materials demonstrate the effectiveness of the proposed pipeline in achieving stable and efficient manipulation.