Regrasping and unfolding of garments using predictive thin shell modeling
Yinxiao Li, Danfei Xu, Yonghao Yue, Yan Wang, Shih-Fu Chang, Eitan Grinspun, Peter K. Allen
Abstract
Deformable objects such as garments are highly unstructured, making them difficult to recognize and manipulate. In this paper, we propose a novel method to teach a two-arm robot to efficiently track the states of a garment from an unknown state to a known state by iterative regrasping. The problem is formulated as a constrained weighted evaluation metric for evaluating the two desired grasping points during regrasping, which can also be used for a convergence criterion The result is then adopted as an estimation to initialize a regrasping, which is then considered as a new state for evaluation. The process stops when the predicted thin shell conclusively agrees with reconstruction. We show experimental results for regrasping a number of different garments including sweater, knitwear, pants, and leggings, etc.
BibTeX
@inproceedings{icra2015_regraspingandunf,
title = {Regrasping and unfolding of garments using predictive thin shell modeling},
author = {Yinxiao Li and Danfei Xu and Yonghao Yue and Yan Wang and Shih-Fu Chang and Eitan Grinspun and Peter K. Allen},
booktitle = {ICRA 2015},
year = {2015}
}