  We address the problem of shaping deformable plastic materials using
non-prehensile actions. Shaping plastic objects is challenging, since they are
difficult to model and to track visually. We study this problem, by using
kinetic sand, a plastic toy material which mimics the physical properties of
wet sand. Inspired by a pilot study where humans shape kinetic sand, we define
two types of actions: \textit{pushing} the material from the sides and
\textit{tapping} from above. The chosen actions are executed with a robotic arm
using image-based visual servoing. From the current and desired view of the
material, we define states based on visual features such as the outer contour
shape and the pixel luminosity values. These are mapped to actions, which are
repeated iteratively to reduce the image error until convergence is reached.
For pushing, we propose three methods for mapping the visual state to an
action. These include heuristic methods and a neural network, trained from
human actions. We show that it is possible to obtain simple shapes with the
kinetic sand, without explicitly modeling the material. Our approach is limited
in the types of shapes it can achieve. A richer set of action types and
multi-step reasoning is needed to achieve more sophisticated shapes.
