  Locomotion is realized through making and breaking contact. State-of-the-art
constrained nonlinear model predictive controllers (NMPC) generate whole-body
trajectories for a given contact sequence. However, these approaches are
computationally expensive at run-time. Hence it is desirable to offload some of
this computation to an offline phase. In this paper, we hypothesize that
conditioning a learned policy on the locations and timings of contact is a
suitable representation for learning a single policy that can generate multiple
gaits (contact sequences). In this way, we can build a single generalist policy
to realize different gaited and non-gaited locomotion skills and the
transitions among them. Our extensive simulation results demonstrate the
validity of our hypothesis for learning multiple gaits for a biped robot.
