Folding deformable objects using predictive simulation and trajectory optimization
Yinxiao Li, Yonghao Yue, Danfei Xu, Eitan Grinspun, Peter K. Allen
Abstract
Robotic manipulation of deformable objects remains a challenging task. One such task is folding a garment autonomously. Given start and end folding positions, what is an optimal trajectory to move the robotic arm to fold a garment? Certain trajectories will cause the garment to move, creating wrinkles, and gaps, other trajectories will fail altogether. We present a novel solution to find an optimal trajectory that avoids such problematic scenarios. The trajectory is optimized by minimizing a quadratic objective function in an off-line simulator, which includes material properties of the garment and frictional force on the table. The function measures the dissimilarity between a user folded shape and the folded garment in simulation, which is then used as an error measurement to create an optimal trajectory. We demonstrate that our two-arm robot can follow the optimized trajectories, achieving accurate and efficient manipulations of deformable objects.
BibTeX
@inproceedings{iros2015_foldingdeformabl,
title = {Folding deformable objects using predictive simulation and trajectory optimization},
author = {Yinxiao Li and Yonghao Yue and Danfei Xu and Eitan Grinspun and Peter K. Allen},
booktitle = {IROS 2015},
year = {2015}
}