Learning and use of sensorimotor schemata maps

Abstract

In this paper we present a framework for the learning and use of sensorimotor schemata. Therefore, we introduce the concept of a schema as a compact representation of an attractor dynamic and discuss how schemata, if embedded into the proposed architecture, can be used to produce, simulate, or recognize goal-directed behaviors. We further present a first implementation of the framework which incorporates well-founded biological principles. Firstly, we apply population coding for the representation of schemata in a neural map and, secondly, we use basis functions as flexible intermediate representations for sensorimotor transformations. Simulation results show that during an initial motor babbling phase the system is able to autonomously develop schemata which correspond to generic behaviors. Moreover, the learned sensorimotor schemata map is topologically ordered insofar as neighboring schemata represent similar behaviors. In accordance with biological findings on the motor system of vertebrates the schemata form a set of behavior primitives which can be flexibly combined to yield more complex behaviors.