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.