  Humans are capable of continuously manipulating a wide variety of deformable
objects into complex shapes. This is made possible by our intuitive
understanding of material properties and mechanics of the object, for reasoning
about object states even when visual perception is occluded. These capabilities
allow us to perform diverse tasks ranging from cooking with dough to expressing
ourselves with pottery-making. However, developing robotic systems to robustly
perform similar tasks remains challenging, as current methods struggle to
effectively model volumetric deformable objects and reason about the complex
behavior they typically exhibit. To study the robotic systems and algorithms
capable of deforming volumetric objects, we introduce a novel robotics task of
continuously deforming clay on a pottery wheel. We propose a pipeline for
perception and pottery skill-learning, called RoPotter, wherein we demonstrate
that structural priors specific to the task of pottery-making can be exploited
to simplify the pottery skill-learning process. Namely, we can project the
cross-section of the clay to a plane to represent the state of the clay,
reducing dimensionality. We also demonstrate a mesh-based method of occluded
clay state recovery, toward robotic agents capable of continuously deforming
clay. Our experiments show that by using the reduced representation with
structural priors based on the deformation behaviors of the clay, RoPotter can
perform the long-horizon pottery task with 44.4% lower final shape error
compared to the state-of-the-art baselines.
