Cloth Manipulation Using Random-Forest-Based Imitation Learning

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

5 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number8633959
Pages (from-to)2086-2093
Journal / PublicationIEEE Robotics and Automation Letters
Volume4
Issue number2
Online published4 Feb 2019
Publication statusPublished - Apr 2019

Abstract

We present a novel approach for manipulating high-DOF deformable objects such as cloth. Our approach uses a random-forest-based controller that maps the observed visual features of the cloth to an optimal control action of the manipulator. The topological structure of this random-forest is determined automatically based on the training data, which consists of visual features and control signals. The training data is constructed online using an imitation learning algorithm. We have evaluated our approach on different cloth manipulation benchmarks such as flattening, folding, and twisting. In all these tasks, we have observed convergent behavior for the random-forest. On convergence, the random-forest-based controller exhibits superior robustness to observation noise compared with other techniques such as convolutional neural networks and nearest neighbor searches. Videos and supplemental material are available at http://gamma.cs.unc.edu/ClothM/.

Research Area(s)

  • Manipulation planning, motion and path planning, simulation and animation

Citation Format(s)

Cloth Manipulation Using Random-Forest-Based Imitation Learning. / Jia, Biao; Pan, Zherong; Hu, Zhe; Pan, Jia; Manocha, Dinesh.

In: IEEE Robotics and Automation Letters, Vol. 4, No. 2, 8633959, 04.2019, p. 2086-2093.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review