  Learning multiple gaits is non-trivial for legged robots, especially when
encountering different terrains and velocity commands. In this work, we present
an end-to-end training framework for learning multiple gaits for quadruped
robots, tailored to the needs of robust locomotion, agile locomotion, and
user's commands. A latent space is constructed concurrently by a gait encoder
and a gait generator, which helps the agent to reuse multiple gait skills to
achieve adaptive gait behaviors. To learn natural behaviors for multiple gaits,
we design gait-dependent rewards that are constructed explicitly from gait
parameters and implicitly from conditional adversarial motion priors (CAMP). We
demonstrate such multiple gaits control on a quadruped robot Go1 with only
proprioceptive sensors.
