Learning Agile Robotic Locomotion Skills by Imitating Animals
Abstract
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 often involves a
tedious engineering process, and requires substantial expertise
of the nuances of each skill. 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 common framework
is able to automatically synthesize controllers for a diverse
repertoire behaviors. By incorporating sample efficient domain
adaptation techniques into the training process, our system is able
to train adaptive policies in simulation, which can then be quickly
finetuned and deployed in the real world. Our system enables an
18-DoF quadruped robot to perform a variety of agile behaviors
ranging from different locomotion gaits to dynamic hops and
turns.
of animals has been a longstanding challenge in robotics. While
manually designed controllers have been able to emulate many
complex behaviors, building such controllers often involves a
tedious engineering process, and requires substantial expertise
of the nuances of each skill. 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 common framework
is able to automatically synthesize controllers for a diverse
repertoire behaviors. By incorporating sample efficient domain
adaptation techniques into the training process, our system is able
to train adaptive policies in simulation, which can then be quickly
finetuned and deployed in the real world. Our system enables an
18-DoF quadruped robot to perform a variety of agile behaviors
ranging from different locomotion gaits to dynamic hops and
turns.
Research Areas
