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.

翻译：从建筑材料（如沙子或沥青）到厨房食材（如大米、糖或盐），世界充满了颗粒物质。尽管机器人操控技术取得了显著进展，但由于这些具有复杂内部动力学的可变材料在感知、表征、建模和规划方面存在困难，操控和与颗粒物质交互仍是一项挑战。虽然已有部分研究致力于估计或学习颗粒物质的精确动力学模型，但文献中仍缺乏一种更抽象的规划方法，可用于针对未知材料属性的颗粒物质规划操控动作。在本工作中，我们利用最优输运工具并将其与机器人运动规划相结合。我们提出了一种基于启发式的扫掠规划器，该规划器无需了解材料属性，直接使用高度图表示来生成有前景的扫掠动作。这些扫掠可将颗粒物质从任意起始形状转变为任意目标形状。我们将扫掠规划器应用于快速且反应式的反馈环路中，无需基于模型的多时间步规划。我们通过大量仿真和硬件实验验证了该方法，结果表明，我们的方法能够高效解决多项复杂任务，包括收集、分离多种类型的颗粒物质并将其塑形为不同目标形状。