  Quadruped locomotion is rapidly maturing to a degree where robots now
routinely traverse a variety of unstructured terrains. However, while gaits can
be varied typically by selecting from a range of pre-computed styles, current
planners are unable to vary key gait parameters continuously while the robot is
in motion. The synthesis, on-the-fly, of gaits with unexpected operational
characteristics or even the blending of dynamic manoeuvres lies beyond the
capabilities of the current state-of-the-art. In this work we address this
limitation by learning a latent space capturing the key stance phases of a
particular gait, via a generative model trained on a single trot style. This
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. In fact properties of this drive signal map directly to
gait parameters such as cadence, footstep height and full stance duration. 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 a real ANYmal quadruped robot and demonstrate that our method
achieves a continuous blend of dynamic trot styles whilst being robust and
reactive to external perturbations.

Robotics;Machine Learning