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

Imitation and Adaptation Based on Consistency: A Quadruped Robot Imitates Animals from Videos Using Deep Reinforcement Learning