高速オンラインガウス過程回帰による3次元変形物体の操作
3D Deformable Object Manipulation using Fast Online Gaussian Process Regression
ロボットの動きと変形物体の変形の対応をガウス過程回帰でオンライン学習し、不要なデータを除去して高速化することで、未知の変形特性を持つ物体の位置と形状を正確にサーボ制御する手法を提案した。
著者: Zhe Hu, Peigen Sun, Jia Pan
分類: cs.RO
原文アブストラクト
In this paper, 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 a wide variety of goal settings. Experiment videos are available at https://sites.google.com/view/mso-fogpr
