  Inverted landing in a rapid and robust manner is a challenging feat for
aerial robots, especially while depending entirely on onboard sensing and
computation. In spite of this, this feat is routinely performed by biological
fliers such as bats, flies, and bees. Our previous work has identified a direct
causal connection between a series of onboard visual cues and kinematic actions
that allow for reliable execution of this challenging aerobatic maneuver in
small aerial robots. In this work, we first utilized Deep Reinforcement
Learning and a physics-based simulation to obtain a general, optimal control
policy for robust inverted landing starting from any arbitrary approach
condition. This optimized control policy provides a computationally-efficient
mapping from the system's observational space to its motor command action
space, including both triggering and control of rotational maneuvers. This was
done by training the system over a large range of approach flight velocities
that varied with magnitude and direction.
  Next, we performed a sim-to-real transfer and experimental validation of the
learned policy via domain randomization, by varying the robot's inertial
parameters in the simulation. Through experimental trials, we identified
several dominant factors which greatly improved landing robustness and the
primary mechanisms that determined inverted landing success. We expect the
learning framework developed in this study can be generalized to solve more
challenging tasks, such as utilizing noisy onboard sensory data, landing on
surfaces of various orientations, or landing on dynamically-moving surfaces.
