Learning and Reuse of Experience in Behavior-Based Service Robots

Abstract

In this paper, we describe the incorporation of learning and reuse of experience into behavior-based service robot systems. Experience is context based, and the learned experience is associated with its relevant behaviors. Object constraints are obtained by virtual object movements in image space. In addition to retaining the key properties of behavior-based systems, our approach also has the planning ability to recover from errors and to imitate an object pattern shown by humans. Experiments on a real physical robot system have successfully tested the approach.