  Inverted landing is a routine behavior among a number of animal fliers.
However, mastering this feat poses a considerable challenge for robotic fliers,
especially to perform dynamic perching with rapid body rotations (or flips) and
landing against gravity. Inverted landing in flies have suggested that optical
flow senses are closely linked to the precise triggering and control of body
flips that lead to a variety of successful landing behaviors. Building upon
this knowledge, we aimed to replicate the flies' landing behaviors in small
quadcopters by developing a control policy general to arbitrary
ceiling-approach conditions. First, we employed reinforcement learning in
simulation to optimize discrete sensory-motor pairs across a broad spectrum of
ceiling-approach velocities and directions. Next, we converted the
sensory-motor pairs to a two-stage control policy in a continuous
augmented-optical flow space. The control policy consists of a first-stage
Flip-Trigger Policy, which employs a one-class support vector machine, and a
second-stage Flip-Action Policy, implemented as a feed-forward neural network.
To transfer the inverted-landing policy to physical systems, we utilized domain
randomization and system identification techniques for a zero-shot sim-to-real
transfer. As a result, we successfully achieved a range of robust
inverted-landing behaviors in small quadcopters, emulating those observed in
flies.
