A cerebellar approach to adaptive locomotion for legged robots

Abstract

This paper describes a neural learning architecture for control of legged robots inspired by mammalian neurophysiology. Biological studies indicate that the cerebellum is a key part of an adaptive control system which enables mammals to display remarkable limb coordination during locomotion. We present a distributed control system using reinforcement learning methods and mechanisms inspired by the cerebellum. Embedded within a framework of base locomotion controllers, the system is tasked with learning modulatory control signals which optimize gait performance measures. We briefly describe simulation studies in progress for a four-legged robot.