  Reinforcement learning (RL) with linear temporal logic (LTL) objectives can
allow robots to carry out symbolic event plans in unknown environments. Most
existing methods assume that the event detector can accurately map
environmental states to symbolic events; however, uncertainty is inevitable for
real-world event detectors. Such uncertainty in an event detector generates
multiple branching possibilities on LTL instructions, confusing action
decisions. Moreover, the queries to the uncertain event detector, necessary for
the task's progress, may increase the uncertainty further. To cope with those
issues, we propose an RL framework, Learning Action and Query over Belief LTL
(LAQBL), to learn an agent that can consider the diversity of LTL instructions
due to uncertain event detection while avoiding task failure due to the
unnecessary event-detection query. Our framework simultaneously learns 1) an
embedding of belief LTL, which is multiple branching possibilities on LTL
instructions using a graph neural network, 2) an action policy, and 3) a query
policy which decides whether or not to query for the event detector.
Simulations in a 2D grid world and image-input robotic inspection environments
show that our method successfully learns actions to follow LTL instructions
even with uncertain event detectors.
