  We consider joint trajectory generation and tracking control for
under-actuated robotic systems. A common solution is to use a layered control
architecture, where the top layer uses a simplified model of system dynamics
for trajectory generation, and the low layer ensures approximate tracking of
this trajectory via feedback control. While such layered control architectures
are standard and work well in practice, selecting the simplified model used for
trajectory generation typically relies on engineering intuition and experience.
In this paper, we propose an alternative data-driven approach to dynamics-aware
trajectory generation. We show that a suitable augmented Lagrangian
reformulation of a global nonlinear optimal control problem results in a
layered decomposition of the overall problem into trajectory planning and
feedback control layers. Crucially, the resulting trajectory optimization is
dynamics-aware, in that, it is modified with a tracking penalty regularizer
encoding the dynamic feasibility of the generated trajectory. We show that this
tracking penalty regularizer can be learned from system rollouts for
independently-designed low layer feedback control policies, and instantiate our
framework in the context of a unicycle and a quadrotor control problem in
simulation. Further, we show that our approach handles the sim-to-real gap
through experiments on the quadrotor hardware platform without any additional
training. For both the synthetic unicycle example and the quadrotor system, our
framework shows significant improvements in both computation time and dynamic
feasibility in simulation and hardware experiments.
