  Accurately simulating real world object dynamics is essential for various
applications such as robotics, engineering, graphics, and design. To better
capture complex real dynamics such as contact and friction, learned simulators
based on graph networks have recently shown great promise. However, applying
these learned simulators to real scenes comes with two major challenges: first,
scaling learned simulators to handle the complexity of real world scenes which
can involve hundreds of objects each with complicated 3D shapes, and second,
handling inputs from perception rather than 3D state information. Here we
introduce a method which substantially reduces the memory required to run
graph-based learned simulators. Based on this memory-efficient simulation
model, we then present a perceptual interface in the form of editable NeRFs
which can convert real-world scenes into a structured representation that can
be processed by graph network simulator. We show that our method uses
substantially less memory than previous graph-based simulators while retaining
their accuracy, and that the simulators learned in synthetic environments can
be applied to real world scenes captured from multiple camera angles. This
paves the way for expanding the application of learned simulators to settings
where only perceptual information is available at inference time.
