Learning Keypoints for Robotic Cloth Manipulation using Synthetic Data

arXiv:2401.01734v2 Announce Type: replace
Abstract: Assistive robots should be able to wash, fold or iron clothes. However, due to the variety, deformability and self-occlusions of clothes, creating robot systems for cloth manipulation is challenging. Synthetic data is a promising direction to improve generalization, but the sim-to-real gap limits its effectiveness. To advance the use of synthetic data for cloth manipulation tasks such as robotic folding, we present a synthetic data pipeline to train keypoint detectors for almost-flattened cloth items. To …

abstract arxiv clothes cs.cv data gap however manipulation replace robot robotic robots sim sim-to-real synthetic synthetic data systems type