  With increasing urban population, there is global interest in Urban Air
Mobility (UAM), where hundreds of autonomous Unmanned Aircraft Systems (UAS)
execute missions in the airspace above cities. Unlike traditional
human-in-the-loop air traffic management, UAM requires decentralized autonomous
approaches that scale for an order of magnitude higher aircraft densities and
are applicable to urban settings. We present Learning-to-Fly (L2F), a
decentralized on-demand airborne collision avoidance framework for multiple UAS
that allows them to independently plan and safely execute missions with
spatial, temporal and reactive objectives expressed using Signal Temporal
Logic. We formulate the problem of predictively avoiding collisions between two
UAS without violating mission objectives as a Mixed Integer Linear Program
(MILP).This however is intractable to solve online. Instead, we develop L2F, a
two-stage collision avoidance method that consists of: 1) a learning-based
decision-making scheme and 2) a distributed, linear programming-based UAS
control algorithm. Through extensive simulations, we show the real-time
applicability of our method which is $\approx\!6000\times$ faster than the MILP
approach and can resolve $100\%$ of collisions when there is ample room to
maneuver, and shows graceful degradation in performance otherwise. We also
compare L2F to two other methods and demonstrate an implementation on
quad-rotor robots.
