In “Learning Agile Robotic Locomotion Skills by Imitating Animals”, we present a framework that takes a reference motion clip recorded from an animal (a dog, in this case) and uses RL to train a control policy that enables a robot to imitate the motion in the real world. By providing the system with different reference motions, we are able to train a quadruped robot to perform a diverse set of agile behaviors, ranging from fast walking gaits to dynamic hops and turns. The policies are trained primarily in simulation, and then transferred to the real world using a latent space adaptation technique that can efficiently adapt a policy using only a few minutes of data from the real robot. All simulations are performed using PyBullet.

See also https://ai.googleblog.com/2020/04/exploring-nature-inspired-robot-agility.html and https://arxiv.org/abs/2004.00784