  A large body of animation research focuses on optimization of movement
control, either as action sequences or policy parameters. However, as
closed-form expressions of the objective functions are often not available, our
understanding of the optimization problems is limited. Building on recent work
on analyzing neural network training, we contribute novel visualizations of
high-dimensional control optimization landscapes; this yields insights into why
control optimization is hard and why common practices like early termination
and spline-based action parameterizations make optimization easier. For
example, our experiments show how trajectory optimization can become
increasingly ill-conditioned with longer trajectories, but parameterizing
control as partial target states---e.g., target angles converted to torques
using a PD-controller---can act as an efficient preconditioner. Both our
visualizations and quantitative empirical data also indicate that neural
network policy optimization scales better than trajectory optimization for long
planning horizons. Our work advances the understanding of movement optimization
and our visualizations should also provide value in educational use.
