  In many robotic tasks, such as drone racing, the goal is to travel through a
set of waypoints as fast as possible. A key challenge for this task is planning
the minimum-time trajectory, which is typically solved by assuming perfect
knowledge of the waypoints to pass in advance. The resulting solutions are
either highly specialized for a single-track layout, or suboptimal due to
simplifying assumptions about the platform dynamics. In this work, a new
approach to minimum-time trajectory generation for quadrotors is presented.
Leveraging deep reinforcement learning and relative gate observations, this
approach can adaptively compute near-time-optimal trajectories for random track
layouts. Our method exhibits a significant computational advantage over
approaches based on trajectory optimization for non-trivial track
configurations. The proposed approach is evaluated on a set of race tracks in
simulation and the real world, achieving speeds of up to 17 m/s with a physical
quadrotor.
