Movements generations in a quadruped robot based on feature extractions of one movement data

Abstract

Recently, a robot that can adapt to various environments has been expected, and the necessity of controlling complicated systems has been increasing. However, there are some issues for controlling the entire system with one controller, so flexibility could be necessary for the controller. Therefore, we examine the control method from the motion data based on behavioral approach. In this study, we extracted and analyzed the latent features of sensors’ and actuators’ data of the robot, and investigated the role of that each feature values play in robot locomotion. We performed experiments using a developed quadruped robot that can acquire multi-point motion information as the movement data, and extracted and analyzed feature values from motion data using the auto-encoder. As a result, we found that it is possible to realize various movement patterns from one specific motion learnings.