Learning new behaviors : Toward a Control Architecture merging Spatial and Temporal modalities

This paper discusses the role of two antagonist neural networks for the learning and control of complex be- haviors composed as a sequence of elementary states. Learning a pathway with a mobile robot or a sequence of actions with a robot arm can be seen either as the result of the learning of a temporal sequence or as the result of the natural dynamics of a sensory-motor system using appearance based approaches for instance. As a result, we will discuss the performances and the complementary features of each system, and propose a unique control architecture embedding both systems for long life learning. I. INTRODUCTION Our long term goal is to design a control architecture allow- ing a robot to learn, as autonomously as possible, sequences of actions related either to spatial or temporal constraint s (dis- placements between places or gestures for instances). Lear ning a behavior is often related to learning by reinforcement, by demonstration or learning by imitation. Learn by imitation has often been considered as a complex behavior, but in previous work we have showed that the imitation can emerge from elementary mechanisms. For example: a robot that learns a "behavior" consisting in moving at different places and performing some very simple but different manipulations of different objects at each places as shown in figure 1. This