  Quadruped locomotion is rapidly maturing to a degree where robots are able to
realise highly dynamic manoeuvres. However, current planners are unable to vary
key gait parameters of the in-swing feet midair. In this work we address this
limitation and show that it is pivotal in increasing controller robustness by
learning a latent space capturing the key stance phases constituting a
particular gait. This is achieved via a generative model trained on a single
trot style, which encourages disentanglement such that application of a drive
signal to a single dimension of the latent state induces holistic plans
synthesising a continuous variety of trot styles. We demonstrate that specific
properties of the drive signal map directly to gait parameters such as cadence,
footstep height and full stance duration. Due to the nature of our approach
these synthesised gaits are continuously variable online during robot
operation. The use of a generative model facilitates the detection and
mitigation of disturbances to provide a versatile and robust planning
framework. We evaluate our approach on two versions of the real ANYmal
quadruped robots and demonstrate that our method achieves a continuous blend of
dynamic trot styles whilst being robust and reactive to external perturbations.
