  The control of granular materials, showing up in many industrial
applications, is a challenging open research problem. Granular material systems
are complex-behavior (as they could have solid-, fluid-, and gas-like
behaviors) and high-dimensional (as they could have many grains/particles with
at least 3 DOF in 3D) systems. Recently, a machine learning-based Graph Neural
Network (GNN) simulator has been proposed to learn the underlying dynamics. In
this paper, we perform an optimal control of a rigid body-driven granular
material system whose dynamics is learned by a GNN model trained by reduced
data generated via a physics-based simulator and Principal Component Analysis
(PCA). We use Differential Dynamic Programming (DDP) to obtain the optimal
control commands that can form granular particles into a target shape. The
model and results are shown to be relatively fast and accurate. The control
commands are also applied to the ground-truth model,(i.e., physics-based
simulator) to further validate the approach.
