  The current state-of-the-art in quadruped locomotion is able to produce
robust motion for terrain traversal but requires the segmentation of a desired
robot trajectory into a discrete set of locomotion skills such as trot and
crawl. In contrast, in this work we demonstrate the feasibility of learning a
single, unified representation for quadruped locomotion enabling continuous
blending between gait types and characteristics. We present Gaitor, which
learns a disentangled representation of locomotion skills, thereby sharing
information common to all gait types seen during training. The structure
emerging in the learnt representation is interpretable in that it is found to
encode phase correlations between the different gait types. These can be
leveraged to produce continuous gait transitions. In addition, foot swing
characteristics are disentangled and directly addressable. Together with a
rudimentary terrain encoding and a learned planner operating in this structured
latent representation, Gaitor is able to take motion commands including desired
gait type and characteristics from a user while reacting to uneven terrain. We
evaluate Gaitor in both simulated and real-world settings on the ANYmal C
platform. To the best of our knowledge, this is the first work learning such a
unified and interpretable latent representation for multiple gaits, resulting
in on-demand continuous blending between different locomotion modes on a real
quadruped robot.
