  Novice pilots find it difficult to operate and land unmanned aerial vehicles
(UAVs), due to the complex UAV dynamics, challenges in depth perception, lack
of expertise with the control interface and additional disturbances from the
ground effect. Therefore we propose a shared autonomy approach to assist pilots
in safely landing a UAV under conditions where depth perception is difficult
and safe landing zones are limited. Our approach comprises of two modules: a
perception module that encodes information onto a compressed latent
representation using two RGB-D cameras and a policy module that is trained with
the reinforcement learning algorithm TD3 to discern the pilot's intent and to
provide control inputs that augment the user's input to safely land the UAV.
The policy module is trained in simulation using a population of simulated
users. Simulated users are sampled from a parametric model with four
parameters, which model a pilot's tendency to conform to the assistant,
proficiency, aggressiveness and speed. We conduct a user study (n = 28) where
human participants were tasked with landing a physical UAV on one of several
platforms under challenging viewing conditions. The assistant, trained with
only simulated user data, improved task success rate from 51.4% to 98.2%
despite being unaware of the human participants' goal or the structure of the
environment a priori. With the proposed assistant, regardless of prior piloting
experience, participants performed with a proficiency greater than the most
experienced unassisted participants.
