  We present GLAS: Global-to-Local Autonomy Synthesis, a provably-safe,
automated distributed policy generation for multi-robot motion planning. Our
approach combines the advantage of centralized planning of avoiding local
minima with the advantage of decentralized controllers of scalability and
distributed computation. In particular, our synthesized policies only require
relative state information of nearby neighbors and obstacles, and compute a
provably-safe action. Our approach has three major components: i) we generate
demonstration trajectories using a global planner and extract local
observations from them, ii) we use deep imitation learning to learn a
decentralized policy that can run efficiently online, and iii) we introduce a
novel differentiable safety module to ensure collision-free operation, thereby
allowing for end-to-end policy training. Our numerical experiments demonstrate
that our policies have a 20% higher success rate than optimal reciprocal
collision avoidance, ORCA, across a wide range of robot and obstacle densities.
We demonstrate our method on an aerial swarm, executing the policy on low-end
microcontrollers in real-time.
