  Learning a locomotion policy for quadruped robots has traditionally been
constrained to a specific robot morphology, mass, and size. The learning
process must usually be repeated for every new robot, where hyperparameters and
reward function weights must be re-tuned to maximize performance for each new
system. Alternatively, attempting to train a single policy to accommodate
different robot sizes, while maintaining the same degrees of freedom (DoF) and
morphology, requires either complex learning frameworks, or mass, inertia, and
dimension randomization, which leads to prolonged training periods. In our
study, we show that drawing inspiration from animal motor control allows us to
effectively train a single locomotion policy capable of controlling a diverse
range of quadruped robots. The robot differences encompass: a variable number
of DoFs, (i.e. 12 or 16 joints), three distinct morphologies, a broad mass
range spanning from 2 kg to 200 kg, and nominal standing heights ranging from
18 cm to 100 cm. Our policy modulates a representation of the Central Pattern
Generator (CPG) in the spinal cord, effectively coordinating both frequencies
and amplitudes of the CPG to produce rhythmic output (Rhythm Generation), which
is then mapped to a Pattern Formation (PF) layer. Across different robots, the
only varying component is the PF layer, which adjusts the scaling parameters
for the stride height and length. Subsequently, we evaluate the sim-to-real
transfer by testing the single policy on both the Unitree Go1 and A1 robots.
Remarkably, we observe robust performance, even when adding a 15 kg load,
equivalent to 125% of the A1 robot's nominal mass.
