  From construction materials, such as sand or asphalt, to kitchen ingredients,
like rice, sugar, or salt; the world is full of granular materials. Despite
impressive progress in robotic manipulation, manipulating and interacting with
granular material remains a challenge due to difficulties in perceiving,
representing, modelling, and planning for these variable materials that have
complex internal dynamics. While some prior work has looked into estimating or
learning accurate dynamics models for granular materials, the literature is
still missing a more abstract planning method that can be used for planning
manipulation actions for granular materials with unknown material properties.
In this work, we leverage tools from optimal transport and connect them to
robot motion planning. We propose a heuristics-based sweep planner that does
not require knowledge of the material's properties and directly uses a height
map representation to generate promising sweeps. These sweeps transform
granular material from arbitrary start shapes into arbitrary target shapes. We
apply the sweep planner in a fast and reactive feedback loop and avoid the need
for model-based planning over multiple time steps. We validate our approach
with a large set of simulation and hardware experiments where we show that our
method is capable of efficiently solving several complex tasks, including
gathering, separating, and shaping of several types of granular materials into
different target shapes.
