Designing Behavior Trees from Goal-Oriented LTLf Formulas
This provides a method for autonomous agents to ensure formal goal compliance, though it is incremental as it builds on existing LTL and behavior tree frameworks.
The paper tackles the problem of synthesizing planners from temporal logic specifications by converting a subset of LTL formulas into behavior trees that guarantee goal satisfaction, demonstrated through alignment exploration and a sequential key-door robot task.
Temporal logic can be used to formally specify autonomous agent goals, but synthesizing planners that guarantee goal satisfaction can be computationally prohibitive. This paper shows how to turn goals specified using a subset of finite trace Linear Temporal Logic (LTL) into a behavior tree (BT) that guarantees that successful traces satisfy the LTL goal. Useful LTL formulas for achievement goals can be derived using achievement-oriented task mission grammars, leading to missions made up of tasks combined using LTL operators. Constructing BTs from LTL formulas leads to a relaxed behavior synthesis problem in which a wide range of planners can implement the action nodes in the BT. Importantly, any successful trace induced by the planners satisfies the corresponding LTL formula. The usefulness of the approach is demonstrated in two ways: a) exploring the alignment between two planners and LTL goals, and b) solving a sequential key-door problem for a Fetch robot.