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Autonomy for robots interacting with sand will enable a wide range ofbeneficial behaviors, from earth moving for construction and farming vehiclesto navigating rough terrain for Mars rovers. The goal of this work is to shapesand into desired forms. Unlike other common autonomous tasks of achievingdesired state of a robot, achieving a desired shape of a continuouslydeformable environment like sand is a much more challenging task. The state ofrobot can be described with a couple of states-x, y, z, roll, pitch, yaw-butthe desired shape of sand can not be described with just a few values. Sand isan aggregation of billions of small particles. After simplifying the model ofsand and tool interaction by looking only at the surface of the heightmap, wecan formulate the problems into something that is still high dimensional(hundreds to thousands of state dimensions) but much more solvable. We show howthis problem can be formulated into a graph search problem and solve it withthe A-star algorithm and report preliminary results on using deep reinforcementlearning methods like Deep Q-Network and Deep Deterministic Policy Gradient.

Developing a Simple Model for Sand-Tool Interaction and Autonomously Shaping Sand