  Recently, learning-based controllers have been shown to push mobile robotic
systems to their limits and provide the robustness needed for many real-world
applications. However, only classical optimization-based control frameworks
offer the inherent flexibility to be dynamically adjusted during execution by,
for example, setting target speeds or actuator limits. We present a framework
to overcome this shortcoming of neural controllers by conditioning them on an
auxiliary input. This advance is enabled by including a feature-wise linear
modulation layer (FiLM). We use model-free reinforcement-learning to train
quadrotor control policies for the task of navigating through a sequence of
waypoints in minimum time. By conditioning the policy on the maximum available
thrust or the viewing direction relative to the next waypoint, a user can
regulate the aggressiveness of the quadrotor's flight during deployment. We
demonstrate in simulation and in real-world experiments that a single control
policy can achieve close to time-optimal flight performance across the entire
performance envelope of the robot, reaching up to 60 km/h and 4.5g in
acceleration. The ability to guide a learned controller during task execution
has implications beyond agile quadrotor flight, as conditioning the control
policy on human intent helps safely bringing learning based systems out of the
well-defined laboratory environment into the wild.
