Extended DMPs Framework for Position and Decoupled Quaternion Learning and Generalization

Abstract Dynamic movement primitives (DMPs) as a robust and efficient 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-effector 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 defined 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 coefficients 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 sufficient capability to handle the multi-space skills in multi-demonstrations.