  The essence of quadrupeds' movements is the movement of the center of
gravity, which has a pattern in the action of quadrupeds. However, the gait
motion planning of the quadruped robot is time-consuming. Animals in nature can
provide a large amount of gait information for robots to learn and imitate.
Common methods learn animal posture with a motion capture system or numerous
motion data points. In this paper, we propose a video imitation adaptation
network (VIAN) that can imitate the action of animals and adapt it to the robot
from a few seconds of video. The deep learning model extracts key points during
animal motion from videos. The VIAN eliminates noise and extracts key
information of motion with a motion adaptor, and then applies the extracted
movements function as the motion pattern into deep reinforcement learning
(DRL). To ensure similarity between the learning result and the animal motion
in the video, we introduce rewards that are based on the consistency of the
motion. DRL explores and learns to maintain balance from movement patterns from
videos, imitates the action of animals, and eventually, allows the model to
learn the gait or skills from short motion videos of different animals and to
transfer the motion pattern to the real robot.
