A hierarchical robot planning framework that generates behavior trees from temporal logic specifications and optimizes dynamic movement primitives to satisfy spatiotemporal constraints while preserving demonstrated motion dynamics.
Designing Behavior Trees from Goal-Oriented LTLf Formulas
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abstract
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.
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BT-TL-DMPs: A Novel Robot TAMP Framework Combining Behavior Tree, Temporal Logic and Dynamical Movement Primitives
A hierarchical robot planning framework that generates behavior trees from temporal logic specifications and optimizes dynamic movement primitives to satisfy spatiotemporal constraints while preserving demonstrated motion dynamics.