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Extended DMPs Framework for Position and Decoupled Quaternion Learning and Generalization

Chinese Journal of Mechanical Engineering 2022年第4期35卷 227-239页

作者： Zhiwei Liao Fei Zhao Gedong Jiang Xuesong Mei State Key Laboratory for Manufacturing System Engineering Xi’an Jiaotong UniversityXi’an 710049China Shaanxi Key Laboratory of Intelligent Robots and School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China

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

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