  Autonomous exploration of unknown environments is a vital function for robots
and has applications in a wide variety of scenarios. Our focus primarily lies
in its application for the task of efficient coverage of unknown environments.
Various methods have been proposed for this task and frontier based methods are
an efficient category in this class of methods. Efficiency is of utmost
importance in exploration and heuristics play a critical role in guiding our
search. In this work we demonstrate the ability of heuristics that are learnt
by imitating clairvoyant oracles. These learnt heuristics can be used to
predict the expected future return from selected states without building search
trees, which are inefficient and limited by on-board compute. We also propose
an additional filter-based heuristic which results in an enhancement in the
performance of the frontier-based planner with respect to certain tasks such as
coverage planning.
