Generating New Lower Abstract Task Operator using Grid-TLI

Abstract

We propose a method of subdividing robot tasks into new lower abstract tasks. The description of robot tasks in an abstract manner is effective for motion planning for complex tasks and teaching robot movements in various environments. However, a more efficient task description may be obtained by using a lower abstraction according to the work environment. We argue that a higher abstract task can be expressed as a new lower abstract subtasks by applying Grid-based Signal Temporal Inference (Grid-TLI). We show that a new task can be completed using the Signal Temporal Logic formula for each cluster. We demonstrated the efficiency of our method through computer simulations using a 2-D security robot task.