The manipulation of deformable objects by robotic systems presents a significant challenge due to their complex and infinite-dimensional configuration spaces. This paper introduces a novel approach to Deformable Object Manipulation (DOM) by emphasizing the identification and manipulation of Structures of Interest (SOIs) in deformable fabric bags. We propose a bimanual manipulation framework that leverages a Graph Neural Network (GNN)-based latent dynamics model to succinctly represent and predict the behavior of these SOIs. Our approach involves constructing a graph representation from partial point cloud data of the object and learning the latent dynamics model that effectively captures the essential deformations of the fabric bag within a reduced computational space. By integrating this latent dynamics model with Model Predictive Control (MPC), we empower robotic manipulators to perform precise and stable manipulation tasks focused on the SOIs. We have validated our framework through various empirical experiments demonstrating its efficacy in bimanual manipulation of fabric bags. Our contributions not only address the complexities inherent in DOM but also provide new perspectives and methodologies for enhancing robotic interactions with deformable objects by concentrating on their critical structural elements. Experimental videos can be obtained from https://sites.google.com/view/bagbot.

翻译：机器人系统对可变形物体的操作因其复杂且无限维的构型空间而面临重大挑战。本文提出一种新颖的可变形物体操作方法，其核心在于识别并操作可变形织物布袋中的感兴趣结构。我们构建了一个双臂操作框架，利用基于图神经网络的潜在动力学模型简洁地表示和预测这些感兴趣结构的行为。该方法通过从物体的部分点云数据中构建图表示，并学习能有效捕捉织物布袋在降维计算空间内关键变形的潜在动力学模型。通过将此潜在动力学模型与模型预测控制集成，我们使机器人操作器能够执行聚焦于感兴趣结构的精确稳定操作任务。通过多项实验验证，该框架在织物布袋的双臂操作中展现出有效性。本文贡献不仅解决了可变形物体操作的内在复杂性，还通过聚焦物体关键结构要素，为增强机器人对可变形物体的交互提供了新视角与方法论。实验视频可从https://sites.google.com/view/bagbot获取。