  We present a new family of deep neural network-based dynamic systems. The
presented dynamics are globally stable and can be conditioned with an arbitrary
context state. We show how these dynamics can be used as structured robot
policies. Global stability is one of the most important and straightforward
inductive biases as it allows us to impose reasonable behaviors outside the
region of the demonstrations.
