  Learning to control robots without requiring engineered models has been a
long-term goal, promising diverse and novel applications. Yet, reinforcement
learning has only achieved limited impact on real-time robot control due to its
high demand of real-world interactions. In this work, by leveraging a learnt
probabilistic model of drone dynamics, we learn a thrust-attitude controller
for a quadrotor through model-based reinforcement learning. No prior knowledge
of the flight dynamics is assumed; instead, a sequential latent variable model,
used generatively and as an online filter, is learnt from raw sensory input.
The controller and value function are optimised entirely by propagating
stochastic analytic gradients through generated latent trajectories. We show
that "learning to fly" can be achieved with less than 30 minutes of experience
with a single drone, and can be deployed solely using onboard computational
resources and sensors, on a self-built drone.
