  In this paper, we examine the problem of robotic manipulation of granular
media. We evaluate multiple predictive models used to infer the dynamics of
scooping and dumping actions. These models are evaluated on a task that
involves manipulating the media in order to deform it into a desired shape. Our
best performing model is based on a highly-tailored convolutional network
architecture with domain-specific optimizations, which we show accurately
models the physical interaction of the robotic scoop with the underlying media.
We empirically demonstrate that explicitly predicting physical mechanics
results in a policy that out-performs both a hand-crafted dynamics baseline,
and a "value-network", which must otherwise implicitly predict the same
mechanics in order to produce accurate value estimates.
