Extended DMPs Framework for Position and Decoupled Quaternion Learning and Generalization  

作　　者：Zhiwei Liao Fei Zhao Gedong Jiang Xuesong Mei 

机构地区：[1]State Key Laboratory for Manufacturing System Engineering,Xi’an Jiaotong University,Xi’an 710049,China [2]Shaanxi Key Laboratory of Intelligent Robots and School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an 710049,China

出　　处：《Chinese Journal of Mechanical Engineering》2022年第4期227-239,共13页中国机械工程学报（英文版）

基　　金：Supported by National Natural Science Foundation of China(Grant No.52175029);Key Industrial Chain Projects of Shaanxi Province(Grant No.2018ZDCXL-GY-06-05).

摘　　要：Dynamic movement primitives(DMPs)as a robust and efcient framework has been studied widely for robot learning from demonstration.Classical DMPs framework mainly focuses on the movement learning in Cartesian or joint space,and can’t properly represent end-efector orientation.In this paper,we present an extended DMPs framework(EDMPs)both in Cartesian space and 2-Dimensional(2D)sphere manifold for Quaternion-based orientation learning and generalization.Gaussian mixture model and Gaussian mixture regression(GMM-GMR)are adopted as the initialization phase of EDMPs to handle multi-demonstrations and obtain their mean and covariance.Additionally,some evaluation indicators including reachability and similarity are defned to characterize the learning and generalization abilities of EDMPs.Finally,a real-world experiment was conducted with human demonstrations,the endpoint poses of human arm were recorded and successfully transferred from human to the robot.The experimental results show that the absolute errors of the Cartesian and Riemannian space skills are less than 3.5 mm and 1.0°,respectively.The Pearson’s correlation coefcients of the Cartesian and Riemannian space skills are mostly greater than 0.9.The developed EDMPs exhibits superior reachability and similarity for the multi-space skills’learning and generalization.This research proposes a fused framework with EDMPs and GMM-GMR which has sufcient capability to handle the multi-space skills in multi-demonstrations.

关 键 词：Learning from demonstration Dynamic movement primitives 2D sphere manifold Gaussian mixture model Gaussian mixture regression Quaternion-based orientation 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]