Clothes manipulation is a critical capability for household robots; yet, existing methods are often confined to specific tasks, such as folding or flattening, due to the complex high-dimensional geometry of deformable fabric. This paper presents CLothes mAnipulation with Semantic keyPoints (CLASP) for general-purpose clothes manipulation, which enables the robot to perform diverse manipulation tasks over different types of clothes. The key idea of CLASP is semantic keypoints -- e.g., "right shoulder", "left sleeve", etc. -- a sparse spatial-semantic representation that is salient for both perception and action. Semantic keypoints of clothes can be effectively extracted from depth images and are sufficient to represent a broad range of clothes manipulation policies. CLASP leverages semantic keypoints to bridge LLM-powered task planning and low-level action execution in a two-level hierarchy. Extensive simulation experiments show that CLASP outperforms baseline methods across diverse clothes types in both seen and unseen tasks. Further, experiments with a Kinova dual-arm system on four distinct tasks -- folding, flattening, hanging, and placing -- confirm CLASP's performance on a real robot.
View on arXiv@article{deng2025_2408.08160,
title={ General-purpose Clothes Manipulation with Semantic Keypoints },
author={ Yuhong Deng and David Hsu },
journal={arXiv preprint arXiv:2408.08160},
year={ 2025 }
}