  We propose a novel planning technique for satisfying tasks specified in
temporal logic in partially revealed environments. We define high-level actions
derived from the environment and the given task itself, and estimate how each
action contributes to progress towards completing the task. As the map is
revealed, we estimate the cost and probability of success of each action from
images and an encoding of that action using a trained neural network. These
estimates guide search for the minimum-expected-cost plan within our model. Our
learned model is structured to generalize across environments and task
specifications without requiring retraining. We demonstrate an improvement in
total cost in both simulated and real-world experiments compared to a
heuristic-driven baseline.
