Three-Dimensional Deformable Object Manipulation Using Fast Online Gaussian Process Regression

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

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)979-986
Journal / PublicationIEEE Robotics and Automation Letters
Volume3
Issue number2
Online published15 Jan 2018
Publication statusPublished - Apr 2018

Abstract

In this letter, we present a general approach to automatically visual servo control the position and shape of a deformable object whose deformation parameters are unknown. The servo control is achieved by online learning a model mapping between the robotic end-effector's movement and the object's deformation measurement. The model is learned using the Gaussian process regression (GPR) to deal with its highly nonlinear property, and once learned, the model is used for predicting the required control at each time step. To overcome GPR's high computational cost while dealing with long manipulation sequences, we implement a fast online GPR by selectively removing uninformative observation data from the regression process. We validate the performance of our controller on a set of deformable object manipulation tasks and demonstrate that our method can achieve effective and accurate servo control for general deformable objects with awide variety of goal settings. Experiment videos are available
at https://sites.google.com/view/mso-fogpr.

Research Area(s)

  • Visual servoing, dual arm manipulation, deformable objects, Gaussian process, model learning for control, LINEAR OBJECTS, CLOTH, MODEL