Orientation Probabilistic Movement Primitives on Riemannian Manifolds

Learning complex robot motions necessarily demands to have models that are able to encode and retrieve full-pose trajectories when tasks are defined in operational spaces. Riemannian ProMPs enables encoding and retrieving of quaternion trajectories, which is relevant when learning skills in task space.

Our method builds on Riemannian manifold theory, and exploits multilinear geodesic regression for estimating the ProMPs parameters. This novel approach makes ProMPs a suitable model for learning complex full-pose robot motion patterns.

Check our paper and video below!