Neuro-Symbolic Acceleration of MILP Motion Planning with Temporal Logic and Chance Constraints

Abstract

Autonomous systems must solve motion planning problems subject to increasingly complex, time-sensitive, and uncertain missions. These problems often involve high-level task specifications, such as temporal logic or chance constraints, which require solving large-scale Mixed-Integer Linear Programs (MILPs). However, existing MILP-based planning methods suffer from high computational cost and limited scalability, hindering their real-time applicability. We propose to use a neuro-symbolic approach to accelerate MILP-based motion planning by leveraging machine learning techniques to guide the solver's symbolic search. Focusing on three representative classes of diverse planning problems - Signal Temporal Logic (STL) specifications, chance constraints formulated via Conformal Predictive Programming (CPP), and Capability Temporal Logic (CaTL) specifications - we demonstrate how graph neural network-based learning methods can guide traditional symbolic MILP solvers in solving challenging planning problems, including branching variable selection and solver parameter configuration. Through extensive experiments, we show that neuro-symbolic search techniques yield scalability gains. Our approach yields substantial improvements across all three classes of planning problems, achieving an average performance gain of about 20% over state-of-the-art solver across key metrics, including runtime and solution quality.