Motor memory: Representation, learning and consolidation

Abstract

Abstract An efficient representation of motor system is vital to robot control and its ability to learn new skills. While the increasing sensor accuracy and the speed of signal processing failed to bridge the gap between the performance of artificial and human sensorimotor systems, the motor memory architecture seems to remain neglected. Despite the advances in robot skill learning, the latter remains limited to predefined tasks and pre-specified embodiment. We propose a new motor memory architecture that enables information sharing between different skills, on-line learning and off-line memory consolidation. We develop an algorithm for learning and consolidation of motor memory and study the space complexity of the representation in the experiments with humanoid robot Nao. Finally, we propose the integration of motor memory with sensor data into a common sensorimotor memory.