  An important challenge in robotics is understanding the interactions between
robots and deformable terrains that consist of granular material. Granular
flows and their interactions with rigid bodies still pose several open
questions. A promising direction for accurate, yet efficient, modeling is using
continuum methods. Also, a new direction for real-time physics modeling is the
use of deep learning. This research advances machine learning methods for
modeling rigid body-driven granular flows, for application to terrestrial
industrial machines as well as space robotics (where the effect of gravity is
an important factor). In particular, this research considers the development of
a subspace machine learning simulation approach. To generate training datasets,
we utilize our high-fidelity continuum method, material point method (MPM).
Principal component analysis (PCA) is used to reduce the dimensionality of
data. We show that the first few principal components of our high-dimensional
data keep almost the entire variance in data. A graph network simulator (GNS)
is trained to learn the underlying subspace dynamics. The learned GNS is then
able to predict particle positions and interaction forces with good accuracy.
More importantly, PCA significantly enhances the time and memory efficiency of
GNS in both training and rollout. This enables GNS to be trained using a single
desktop GPU with moderate VRAM. This also makes the GNS real-time on
large-scale 3D physics configurations (700x faster than our continuum method).

Machine Learning;Soft Condensed Matter;Robotics