Maximally satisfying LTL action planning

We focus on autonomous robot action planning problem from Linear Temporal Logic (LTL) specifications, where the action refers to a “simple” motion or manipulation task, such as “go from A to B” or “grasp a ball”. At the high-level planning layer, we propose an algorithm to synthesize a maximally satisfying discrete control strategy while taking into account that the robot´s action executions may fail. Furthermore, we interface the high-level plan with the robot´s low-level controller through a reactive middle-layer formalism called Behavior Trees (BTs). We demonstrate the proposed framework using a NAO robot capable of walking, ball grasping and ball dropping actions.