  We present Ungar, an open-source library to aid the implementation of
high-dimensional optimal control problems (OCPs). We adopt modern template
metaprogramming techniques to enable the compile-time modeling of complex
systems while retaining maximum runtime efficiency. Our framework provides
syntactic sugar to allow for expressive formulations of a rich set of
structured dynamical systems. While the core modules depend only on the
header-only Eigen and Boost.Hana libraries, we bundle our codebase with
optional packages and custom wrappers for automatic differentiation, code
generation, and nonlinear programming. Finally, we demonstrate the versatility
of Ungar in various model predictive control applications, namely, four-legged
locomotion and collaborative loco-manipulation with multiple one-armed
quadruped robots. Ungar is available under the Apache License 2.0 at
https://github.com/fdevinc/ungar.
