Control Structure for Learninglocomotion Gaits

Learning and adaptation are important for robot systems operating in the real world in order to react to changes in the task requirements. In many domains this involves the acquisition of cyclic behavioral patterns requiring repetitive control strategies. In the domain of legged locomotion in particular, walking gaits must be acquired. This paper presents a hybrid control architecture which allows to learn such cyclic control strategies while providing reactivity at the lowest level through a set of feedback controllers. The use of reinforcement learning on top of a Discrete Event Dynamic System (DEDS) model of the system behavior furthermore permits to learn such gaits in a single trial using a simple reinforcement structure while maintaining the safety of the mechanism through the imposition of safety constraints. To illustrate this approach it has been applied to the learning of a turning and a forward walking gait on a quadruped robot.