FlingFlow: LLM-Driven Dynamic Strategies for Efficient Cloth Flattening

摘要

The proficiency of robots in cloth manipulation is crucial for their potential widespread deployment in household service contexts, with the task of unfolding cloth being particularly indispensable. Unlike rigid objects, cloth has a high-dimensional state space, which poses significant challenges for robotic operations. This paper presents a robotic framework that integrates dynamic and static operations for cloth unfolding. Dynamic operations are introduced in a single-arm scenario, employing gravity to expedite flattening. Initially, we define the classification of cloth states and operational skills. Subsequently, in skill selection, a Large Language Model (LLM) is utilized to make decisions based on the current state, selecting skills appropriate for the given situation. For the determination of operation points, a cloth region segmentation network extracts key features of the cloth, and the final operation points are determined through geometric analysis of the masks. Experiments on a real robot demonstrate that our method can successfully unfold cloths of various initial conditions, colors, sizes, textures, shapes and materials, achieving over 95<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> coverage - defined as the ratio of the current area of the fabric to its fully expanded area - thereby proving the effectiveness of the combined dynamic and static operation strategy. Furthermore, this method significantlyenhances the efficiency of cloth unfolding, completing the task within ten actions, whereas other methods require dozens of operations, greatly reducing the required operational complexity.