  Reproducing the diverse and agile locomotion skills of animals has been a
longstanding challenge in robotics. While manually-designed controllers have
been able to emulate many complex behaviors, building such controllers involves
a time-consuming and difficult development process, often requiring substantial
expertise of the nuances of each skill. Reinforcement learning provides an
appealing alternative for automating the manual effort involved in the
development of controllers. However, designing learning objectives that elicit
the desired behaviors from an agent can also require a great deal of
skill-specific expertise. In this work, we present an imitation learning system
that enables legged robots to learn agile locomotion skills by imitating
real-world animals. We show that by leveraging reference motion data, a single
learning-based approach is able to automatically synthesize controllers for a
diverse repertoire behaviors for legged robots. By incorporating sample
efficient domain adaptation techniques into the training process, our system is
able to learn adaptive policies in simulation that can then be quickly adapted
for real-world deployment. To demonstrate the effectiveness of our system, we
train an 18-DoF quadruped robot to perform a variety of agile behaviors ranging
from different locomotion gaits to dynamic hops and turns.
