Paper ID: 2303.00323

DeFNet: Deconstructed Strategy for Multi-step Fabric Folding Tasks

Ningquan Gu, Ruhan He, Lianqing Yu

Fabric folding through robots is complex and challenging due to the deformability of fabric. Based on deconstruction strategy, we split the complex fabric folding task into three relatively simple sub-tasks, and propose a Deconstructed Fabric Folding Network (DeFNet), including corresponding three modules to solve them. (1) We use the Folding Planning Module (FPM), which is based on Latent Space Roadmap, to infer the most straight folding intermediate states from the start to the goal in latent space. (2) We utilize the flow-based approach, Folding Action Module (FAM), to calculate the action coordinates and execute them to reach the inferred intermediate state. (3) We introduce an Iterative Interactive Module (IIM) for fabric folding tasks, which can iteratively execute the FPM and FAM after every grasp-and-place action until the fabric reaches the goal. Experimentally, We demonstrated our method on multi-step fabric folding tasks against three baselines in simulation. We also apply the method to an existing robotic system and present its performance.

Submitted: Mar 1, 2023