Transfer Planning for Temporal Logic Tasks

This paper proposes an optimal control synthesis algorithm for Linear Temporal Logic (LTL) tasks that exploits experience from solving similar LTL tasks before. The key idea is to appropriately decompose complex LTL tasks into simpler subtasks and define sets of skills, or plans, needed to solve these subtasks. These skills can be stored in a library of reusable skills and can be used to quickly synthesize plans for new tasks that have not been encountered before. Our proposed method is inspired by literature on multi-task learning and can be used to transfer experience between different LTL tasks. It amounts to a new paradigm in model-checking and optimal control synthesis methods that to this date do not use prior experience to solve planning problems. We present numerical experiments that show that our approach generally outperforms these methods in terms of time to generate feasible plans. We also show that our proposed algorithm is probabilistically complete and asymptotically optimal.