FoldNet: Learning Generalizable Closed-Loop Policy for Garment Folding via Keypoint-Driven Asset and Demonstration Synthesis

Understand

Due to the deformability of garments, generating a large amount of high-quality data for robotic garment manipulation tasks is highly challenging.

• In this paper, we present a synthetic garment dataset that can be used for robotic garment folding.
• We begin by constructing geometric garment templates based on keypoints and applying generative models to generate realistic texture patterns.
• Leveraging these keypoint annotations, we generate folding demonstrations in simulation and train folding policies via closed-loop imitation learning.