  Recent studies have demonstrated the immense potential of exploiting muscle
actuator morphology for natural and robust movement -- in simulation. A
validation on real robotic hardware is yet missing. In this study, we emulate
muscle actuator properties on hardware in real-time, taking advantage of modern
and affordable electric motors. We demonstrate that our setup can emulate a
simplified muscle model on a real robot while being controlled by a learned
policy. We improve upon an existing muscle model by deriving a damping rule
that ensures that the model is not only performant and stable but also tuneable
for the real hardware. Our policies are trained by reinforcement learning
entirely in simulation, where we show that previously reported benefits of
muscles extend to the case of quadruped locomotion and hopping: the learned
policies are more robust and exhibit more regular gaits. Finally, we confirm
that the learned policies can be executed on real hardware and show that
sim-to-real transfer with real-time emulated muscles on a quadruped robot is
possible. These results show that artificial muscles can be highly beneficial
actuators for future generations of robust legged robots.
