Self-organizing approach for robot's behavior imitation

Abstract

In this paper, an approach for behavior imitation using visual information was introduced. The imitation process is done by a self organizing neural network module. From several demonstrations of task operation, a vision system captures movement of the demonstrator mobile robot and associated objects in an operation field. Then, the movement features are extracted to present to an imitation engine. Finally, skill or decision policy from teacher's demonstration is extracted and embedded into a self organizing neural network without explicit external supervisory signals. A simple action selection algorithm for choosing action from learned network is proposed. The algorithm was implemented and tested on a simulated robot and a real mobile robot to imitate two simple robot soccer behaviors: approaching the target and obstacle avoidance. Furthermore, the concept of similarity measure is introduced to evaluate imitation performance from the demonstrator