Learning robot motions with stable dynamical systems under diffeomorphic transformations
A novel method named tau-SEDS that learns stable robot motions from demonstrations on the basis of nonlinear dynamical systems is proposed. The method allows learning dynamical systems that are stable and accurate by construction without much tuning effort. The approach is rigorously analyzed within a substantial theoretical framework. Several robotics experiments underline the applicability of the method.
robotics dynamical system seds imitation learning programming by demonstration stability