Walking pattern acquisition for quadruped robot by using modular reinforcement learning

We apply the reinforcement learning to acquire a gait pattern of a quadruped locomotion robot. The advantage of the reinforcement learning for such a problem is that no exact robot model is needed for calculating the prescribed teaching signals, but one simply needs to evaluate the results of trials and generates the reinforcement signals. As a result, the robot can acquire by itself a walking pattern suitable to its structure, dynamics and environments. We use here a tightly coupled modular actor-critic structure with stochastic gradient ascent. The computer simulations show that it could generate various stable walking pattern suitable to the environment and dynamics of the robot. We also apply the proposed method to an experimental real robot and deal with the learning process for getting the walking pattern.