  Real-time adaptation is imperative to the control of robots operating in
complex, dynamic environments. Adaptive control laws can endow even nonlinear
systems with good trajectory tracking performance, provided that any uncertain
dynamics terms are linearly parameterizable with known nonlinear features.
However, it is often difficult to specify such features a priori, such as for
aerodynamic disturbances on rotorcraft or interaction forces between a
manipulator arm and various objects. In this paper, we turn to data-driven
modeling with neural networks to learn, offline from past data, an adaptive
controller with an internal parametric model of these nonlinear features. Our
key insight is that we can better prepare the controller for deployment with
control-oriented meta-learning of features in closed-loop simulation, rather
than regression-oriented meta-learning of features to fit input-output data.
Specifically, we meta-learn the adaptive controller with closed-loop tracking
simulation as the base-learner and the average tracking error as the
meta-objective. With both fully-actuated and underactuated nonlinear planar
rotorcraft subject to wind, we demonstrate that our adaptive controller
outperforms other controllers trained with regression-oriented meta-learning
when deployed in closed-loop for trajectory tracking control.
