  We present a learning algorithm for training a single policy that imitates
multiple gaits of a walking robot. To achieve this, we use and extend MPC-Net,
which is an Imitation Learning approach guided by Model Predictive Control
(MPC). The strategy of MPC-Net differs from many other approaches since its
objective is to minimize the control Hamiltonian, which derives from the
principle of optimality. To represent the policies, we employ a
mixture-of-experts network (MEN) and observe that the performance of a policy
improves if each expert of a MEN specializes in controlling exactly one mode of
a hybrid system, such as a walking robot. We introduce new loss functions for
single- and multi-gait policies to achieve this kind of expert selection
behavior. Moreover, we benchmark our algorithm against Behavioral Cloning and
the original MPC implementation on various rough terrain scenarios. We validate
our approach on hardware and show that a single learned policy can replace its
teacher to control multiple gaits.
