Integration of soft computing towards autonomous legged robots
Autonomous robotic systems
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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.