Incremental Learning of Full-Pose Via-Point Movement Primitives on Riemannian Manifolds

Movement primitives (MPs) are compact repre- sentations of robot skills that can be learned from demonstra- tions and combined into complex behaviors. However, merely equipping robots with a fixed set of innate MPs is insufficient to deploy them in dynamic and unpredictable environments. Instead, the full potential of MPs remains to be attained via adaptable, large-scale MP libraries. In this paper, we propose a set of seven fundamental operations to incrementally learn, improve, and re-organize MP libraries. To showcase their applicability, we provide explicit formulations of the five spatial operations for libraries composed of Via-Point Move- ment Primitives (VMPs). By building on Riemannian manifold theory, our approach enables the incremental learning of all parameters of position and orientation VMPs within a library. Moreover, our approach stores a fixed number of parameters, thus complying with the essential principles of incremental learning. We evaluate our approach to incrementally learn a VMP library from sequentially-provided motion capture data.