Representation and generalization of bi-manual skills from kinesthetic teaching

The paper presents a modular architecture for bi-manual skill acquisition from kinesthetic teaching. Skills are learned and embedded over several representational levels comprising a compact movement representation by means of movement primitives, a task space description of the bi-manual tool constraint, and the particular redundancy resolution of the inverse kinematics. A comparative evaluation of different architectural configurations identifies a specific modulation scheme for skill execution to achieve optimal spatial generalization from few training samples. Based on this architectural layout together with a novel stabilization approach for dynamical movement primitives, the robust teaching and execution of complex skill sequences is demonstrated on the humanoid robot iCub.