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We present a framework that, given a set of skills a robot can perform,abstracts sensor data into symbols that we use to automatically encode therobot's capabilities in Linear Temporal Logic. We specify reactive high-leveltasks based on these capabilities, for which a strategy is automaticallysynthesized and executed on the robot, if the task is feasible. If a task isnot feasible given the robot's capabilities, we present two methods, oneenumeration-based and one synthesis-based, for automatically suggestingadditional skills for the robot or modifications to existing skills that wouldmake the task feasible. We demonstrate our framework on a Baxter robotmanipulating blocks on a table, a Baxter robot manipulating plates on a table,and a Kinova arm manipulating vials, with multiple sensor modalities, includingraw images.

Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations