Finding Missing Skills for High-Level Behaviors

Recently, Linear Temporal Logic (LTL) has been used as a formalism for defining high-level robot tasks, and LTL synthesis has been used to automatically create correct-by-construction robot control. The underlying premise of this approach is that the robot has a set of actions, or skills, that can be composed to achieve the high- level task. In this paper we consider LTL specifications that cannot be synthesized into robot control due to lack of appropriate skills; we present algorithms for automatically suggesting new or modified skills for the robot that will guarantee the task will be achieved. We demonstrate our approach with a physical Baxter robot and a simulated KUKA IIWA arm.