  In recent years, reinforcement learning (RL) has shown outstanding
performance for locomotion control of highly articulated robotic systems. Such
approaches typically involve tedious reward function tuning to achieve the
desired motion style. Imitation learning approaches such as adversarial motion
priors aim to reduce this problem by encouraging a pre-defined motion style. In
this work, we present an approach to augment the concept of adversarial motion
prior-based RL to allow for multiple, discretely switchable styles. We show
that multiple styles and skills can be learned simultaneously without notable
performance differences, even in combination with motion data-free skills. Our
approach is validated in several real-world experiments with a wheeled-legged
quadruped robot showing skills learned from existing RL controllers and
trajectory optimization, such as ducking and walking, and novel skills such as
switching between a quadrupedal and humanoid configuration. For the latter
skill, the robot is required to stand up, navigate on two wheels, and sit down.
Instead of tuning the sit-down motion, we verify that a reverse playback of the
stand-up movement helps the robot discover feasible sit-down behaviors and
avoids tedious reward function tuning.
