A Neural Network Based Hierarchical Motor Schema of a Multi-finger Hand and Its Motion Diversity

Abstract

This paper presents a neural network based hierarchical motor schema of a multi finger hand to generate suitable behavior for an unknown situation without retraining all neural networks and investigates its motion diversity by changing its input signals. Conventional neural networks are hard to generate desired movements in an unknown situation. Our hierarchical motor schema consists of the two layers. A lower schema is implemented by a recurrent neural network trained with primitive movement patterns and generates a finger movement from a command code sent from the upper schema. The upper schema generates command codes to each finger from a behavior command code such as grasping. We showed that though the lower schemata were fixed, diversity of generated finger movements can be obtained by changing a behavior code of the upper schema through computer simulation.