Curiosity based Self-Organization of Humanoid Robot

Abstract

This thesis presents a novel approach to how a high dimensional humanoid robot of 18 dimensions can learn within a few hours to control its body so that it is able to perform simple tasks such as rolling around or to sit up. The method is robust and works equally well when an arm is removed, and in a case where the robot was trained to use two arms and one was removed it quickly adapted to its new body. The robot is equipped with an accelerometer that measures the tilt of the torso in 2 dimensions. This "tilt"-space is divided into a discrete set of states, and the way in which the dimensionality of the servo-space is made irrelevant is to only allow one servo-con guration per state. These con gurations are evolved using a Self-Organizing Map, while an Arti cial Curiosity-driven Reinforcement Learner chooses what state to state transitions to attempt. An additional parameter is added in a nal experiment, to see if the agent can even learn to stand. This experiment was however unsuccessful.