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

TL;DR

This paper introduces VIAN, a deep learning framework that enables quadruped robots to imitate animal gaits from short videos using reinforcement learning, reducing the need for extensive motion data.

Contribution

The novel VIAN model combines key point extraction from videos with reinforcement learning to efficiently transfer animal gait patterns to robots from minimal video data.

Findings

Successfully imitates animal gaits from short videos

Achieves real-time gait adaptation on quadruped robots

Reduces data collection effort for robot gait learning

Abstract

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.