Scalable Integrated Task and Motion Planning from Signal Temporal Logic Specifications

TL;DR

This paper introduces a scalable, provably complete algorithm for synthesizing continuous trajectories that satisfy complex, non-convex STL specifications in safety-critical systems, improving over prior methods.

Contribution

It presents a novel integrated task and motion planning algorithm that directly handles non-convex STL specifications without prior discretization, using SMT and LP solvers.

Findings

Algorithm is scalable to high-dimensional systems.

Proved to be sound and complete.

Simulation results demonstrate effectiveness.

Abstract

Many safety-critical systems must achieve high-level task specifications with guaranteed safety and correctness. Much recent progress towards this goal has been made through controller synthesis from signal temporal logic (STL) specifications. Existing approaches, however, either consider some a priori discretization of the state-space, deal only with a convex fragment of STL, or are not provably complete. We propose a scalable, provably complete algorithm that directly synthesizes continuous trajectories to satisfy non-convex STL specifications. We separate discrete task planning and continuous motion planning on the fly and harness highly efficient satisfiability modulo theories (SMT) and linear programming (LP) solvers to find dynamically feasible trajectories for high dimensional systems that satisfies non-convex STL specifications. The proposed design algorithms are proved sound and complete, and simulation results demonstrate the scalability of our approach.