  The deployment of agile autonomous systems in challenging, unstructured
environments requires adaptation capabilities and robustness to uncertainties.
Existing robust and adaptive controllers, such as those based on model
predictive control (MPC), can achieve impressive performance at the cost of
heavy online onboard computations. Strategies that efficiently learn robust and
onboard-deployable policies from MPC have emerged, but they still lack
fundamental adaptation capabilities. In this work, we extend an existing
efficient Imitation Learning (IL) algorithm for robust policy learning from MPC
with the ability to learn policies that adapt to challenging model/environment
uncertainties. The key idea of our approach consists in modifying the IL
procedure by conditioning the policy on a learned lower-dimensional
model/environment representation that can be efficiently estimated online. We
tailor our approach to the task of learning an adaptive position and attitude
control policy to track trajectories under challenging disturbances on a
multirotor. Evaluations in simulation show that a high-quality adaptive policy
can be obtained in about $1.3$ hours. We additionally empirically demonstrate
rapid adaptation to in- and out-of-training-distribution uncertainties,
achieving a $6.1$ cm average position error under wind disturbances that
correspond to about $50\%$ of the weight of the robot, and that are $36\%$
larger than the maximum wind seen during training.
