A Distributed Adaptive Control System for a Quadruped Mobile Robot

Abstract

A method by which reinforcement learning can be combined into a behavior based control system is presented. Behaviors which are impossible or impractical to embed as predetermined responses are learned through self-exploration and self-organization using a temporal difference reinforcement learning technique. This results in what is referred to as a distributed adaptive control system (DACS), which is, in effect, the robot's artificial nervous system. A DACS is developed for a simulated quadruped mobile robot and the locomotion behavior level is isolated and evaluated. At the locomotion level the proper actuator sequences are learned for all possible gaits and eventually graceful gait transitions are also learned. When confronted with an actuator malfunction, all gaits and transitions are adapted resulting in new limping gaits for the quadruped. >