We present a novel learning scheme to imprint stable vector fields into Extreme Learning Machines (ELMs). The networks represent movements, where asymptotic stability is incorporated through constraints derived from Lyapunov stability theory. We show that our approach successfully performs stable and smooth point-to-point movements learned from human handwriting movements.