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.

翻译：人类能够持续地将各种可变形物体塑造成复杂形态。这种能力源于我们对材料特性及物体力学性质的直观理解，即使在视觉感知被遮挡时仍能推断物体状态。这些能力使我们能够完成从面团烹饪到陶艺创作等多样化任务。然而，开发能够稳健执行类似任务的机器人系统仍具挑战性，因为现有方法难以有效建模体积型可变形物体并推演其典型复杂行为。为研究能够变形体积物体的机器人系统与算法，我们引入一项在陶轮上连续变形黏土的新型机器人任务。我们提出名为RoPotter的感知与陶艺技能学习框架，通过实验证明：利用陶艺制作任务特有的结构先验可简化技能学习过程。具体而言，通过将黏土横截面投影至平面来表征黏土状态，实现维度约简。我们还提出基于网格的遮挡黏土状态重建方法，以构建能够连续变形黏土的智能体。实验表明：通过采用基于黏土变形行为的结构先验简化表征，RoPotter在执行长时程陶艺任务时，最终形状误差较现有最优基线方法降低44.4%。