Shavit, YonadavFigueroa Fernandez, Nadia BarbaraMirrazavi Salehian, Seyed SinaBillard, Aude2018-05-062018-05-062018-05-062018-05-0610.1109/LRA.2018.2833497https://infoscience.epfl.ch/handle/20.500.14299/146324In this paper, we propose an asymptotically stable joint-space dynamical system that captures desired behaviors in joint-space while stably converging towards a task-space attractor. To encode joint-space behaviors while meeting the stability criteria, the dynamical system is constructed as a Linear Parameter Varying (LPV) system combining different motor synergies, and we provide a method for learning these synergy matrices from demonstrations. Specifically, we use dimensionality reduction to find a low-dimensional embedding space for modulating joint synergies, and then estimate the parameters of the corresponding synergies by solving a convex semi-definite optimization problem that minimizes the joint velocity prediction error from the demonstrations. Our proposed approach is empirically validated on a variety of motions.Motion ControlLearning from DemonstrationKinematicsJoint-Space Controltext::journal::journal article::research article