Fabric manipulation dynamically is commonly seen in manufacturing and domestic settings. While dynamically manipulating a fabric piece to reach a target state is highly efficient, this task presents considerable challenges due to the varying properties of different fabrics, complex dynamics when interacting with environments, and meeting required goal conditions. To address these challenges, we present \textit{One Fling to Goal}, an algorithm capable of handling fabric pieces with diverse shapes and physical properties across various scenarios. Our method learns a graph-based dynamics model equipped with environmental awareness. With this dynamics model, we devise a real-time controller to enable high-speed fabric manipulation in one attempt, requiring less than 3 seconds to finish the goal-conditioned task. We experimentally validate our method on a goal-conditioned manipulation task in five diverse scenarios. Our method significantly improves this goal-conditioned task, achieving an average error of 13.2mm in complex scenarios. Our method can be seamlessly transferred to real-world robotic systems and generalized to unseen scenarios in a zero-shot manner.

翻译：动态织物操作在工业制造与家庭环境中十分常见。虽然通过动态操作使织物达到目标状态具有极高效率，但由于不同织物的物理特性差异、与环境交互时的复杂动力学行为以及需满足特定目标条件，该任务面临显著挑战。为解决这些挑战，我们提出\textit{一抛即达}算法，该算法能够处理多种场景下不同形状与物理特性的织物。我们的方法学习具备环境感知能力的图结构动力学模型。基于该动力学模型，我们设计了实时控制器以实现单次高速织物操作，完成目标条件任务仅需不足3秒。我们在五种不同场景的目标条件操作任务中通过实验验证了本方法。该方法显著提升了目标条件任务的性能，在复杂场景中平均误差达到13.2毫米。本方法可无缝迁移至真实机器人系统，并以零样本方式泛化至未见场景。