Learning Task-Space Synergies using Riemannian Geometry
This video illustrates an approach for learning task-space synergies by demonstration. To transfer the skill a number of kinesthetic demonstrations are given. We encode the skill in a single Gaussian using the data of the two end-effector poses by relying on Riemannian geometry. We then demonstrate the reproduction of the demonstrated skill using LQR in a tangent space of the manifold.
Video credit: Martijn Zeestraten