  Autonomous drones can operate in remote and unstructured environments,
enabling various real-world applications. However, the lack of effective
vision-based algorithms has been a stumbling block to achieving this goal.
Existing systems often require hand-engineered components for state estimation,
planning, and control. Such a sequential design involves laborious tuning,
human heuristics, and compounding delays and errors. This paper tackles the
vision-based autonomous-drone-racing problem by learning deep sensorimotor
policies. We use contrastive learning to extract robust feature representations
from the input images and leverage a two-stage learning-by-cheating framework
for training a neural network policy. The resulting policy directly infers
control commands with feature representations learned from raw images, forgoing
the need for globally-consistent state estimation, trajectory planning, and
handcrafted control design. Our experimental results indicate that our
vision-based policy can achieve the same level of racing performance as the
state-based policy while being robust against different visual disturbances and
distractors. We believe this work serves as a stepping-stone toward developing
intelligent vision-based autonomous systems that control the drone purely from
image inputs, like human pilots.
