  This work develops a zero-shot mechanism for an agent to satisfy a Linear
Temporal Logic (LTL) specification given existing task primitives. Oftentimes,
autonomous robots need to satisfy spatial and temporal goals that are unknown
until run time. Prior research addresses the problem by learning policies that
are capable of executing a high-level task specified using LTL, but they
incorporate the specification into the learning process; therefore, any change
to the specification requires retraining the policy. Other related research
addresses the problem by creating skill-machines which, given a specification
change, do not require full policy retraining but require fine-tuning on the
skill-machine to guarantee satisfaction. We present a more a flexible approach
-- to learn a set of minimum-violation (MV) task primitive policies that can be
used to satisfy arbitrary LTL specifications without retraining or fine-tuning.
Task primitives can be learned offline using reinforcement learning (RL)
methods and combined using Boolean composition at deployment. This work focuses
on creating and pruning a transition system (TS) representation of the
environment in order to solve for deterministic, non-ambiguous, and feasible
solutions to LTL specifications given an environment and a set of MV task
primitive policies. We show that our pruned TS is deterministic, contains no
unrealizable transitions, and is sound. Through simulation, we show that our
approach is executable and we verify our MV policies produce the expected
symbols.
