Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations
Abstract
We present a framework for the automatic encoding and repair of high-level tasks. Given a set of skills a robot can perform, our approach first abstracts sensor data into symbols and then automatically encodes the robot’s capabilities in Linear Temporal Logic (LTL). Using this encoding, a user can specify reactive high-level tasks, for which we can automatically synthesize a strategy that executes on the robot, if the task is feasible. If a task is not feasible given the robot’s capabilities, we present two methods, one enumeration-based and one synthesis-based, for automatically suggesting additional skills for the robot or modifications to existing skills that would make the task feasible. We demonstrate our framework on a Baxter robot manipulating blocks on a table, a Baxter robot manipulating plates on a table, and a Kinova arm manipulating vials, with multiple sensor modalities, including raw images.
