Learning to Hang Crumpled Garments with Confidence-Guided Grasping and Active Perception
Shengzeng Huo, He Zhang, Hoi-Yin Lee, Peng Zhou, David Navarro-Alarcon
Abstract
Accurately recognizing the structural regions of targeted objects is crucial for successful manipulation. In this study, we concentrate on the task of hanging crumpled garments on a rack, a common scenario in household environments. This context presents two primary challenges: (1) perceiving and grasping the structural regions of garments that exhibit severe deformations and self-occlusions; (2) adjusting the configuration of garments to fit the supporting components of the rack. To address these challenges, we propose a confidence-guided grasping strategy that actively seeks garment collars through handovers between dual robotic arms. In particular, we develop an autonomous data collection procedure in real-world settings to train the collar detection network. The exact grasping pose is determined through depth-aware contour extraction, and its success is evaluated based on a specially designed metric. Furthermore, we formulate the hanging task as one-shot imitation learning with an egocentric view. To precisely align the collar with the supporting item, we propose a two-step hanging strategy that involves coarse approaching followed by fine transformation. We perform comprehensive experiments and show that our framework notably enhances the success rate compared to existing methods.
BibTeX
@inproceedings{iros2025_learningtohangcr,
title = {Learning to Hang Crumpled Garments with Confidence-Guided Grasping and Active Perception},
author = {Shengzeng Huo and He Zhang and Hoi-Yin Lee and Peng Zhou and David Navarro-Alarcon},
booktitle = {IROS 2025},
year = {2025}
}