  In this work, we investigate task planning for mobile robots under linear
temporal logic (LTL) specifications. This problem is particularly challenging
when robots navigate in continuous workspaces due to the high computational
complexity involved. Sampling-based methods have emerged as a promising avenue
for addressing this challenge by incrementally constructing random trees,
thereby sidestepping the need to explicitly explore the entire state-space.
However, the performance of this sampling-based approach hinges crucially on
the chosen sampling strategy, and a well-informed heuristic can notably enhance
sample efficiency. In this work, we propose a novel neural-network guided
(NN-guided) sampling strategy tailored for LTL planning. Specifically, we
employ a multi-modal neural network capable of extracting features concurrently
from both the workspace and the B\"{u}chi automaton. This neural network
generates predictions that serve as guidance for random tree construction,
directing the sampling process toward more optimal directions. Through
numerical experiments, we compare our approach with existing methods and
demonstrate its superior efficiency, requiring less than 15% of the time of the
existing methods to find a feasible solution.
