Bagging operations, common in packaging and assisted living applications, are challenging due to a bag's complex deformable properties. To address this, we develop a robotic system for automated bagging tasks using an adaptive structure-of-interest (SOI) manipulation approach. Our method relies on real-time visual feedback to dynamically adjust manipulation without requiring prior knowledge of bag materials or dynamics. We present a robust pipeline featuring state estimation for SOIs using Gaussian Mixture Models (GMM), SOI generation via optimization-based bagging techniques, SOI motion planning with Constrained Bidirectional Rapidly-exploring Random Trees (CBiRRT), and dual-arm manipulation coordinated by Model Predictive Control (MPC). Experiments demonstrate the system's ability to achieve precise, stable bagging of various objects using adaptive coordination of the manipulators. The proposed framework advances the capability of dual-arm robots to perform more sophisticated automation of common tasks involving interactions with deformable objects.

翻译：装袋操作常见于包装和辅助生活应用中，但由于袋子复杂的可变形特性而颇具挑战。为此，我们开发了一种采用自适应兴趣结构（SOI）操控方法的机器人系统，用于自动化装袋任务。该方法依赖实时视觉反馈动态调整操控，无需预先了解袋子材料或动力学特性。我们提出一个稳健的流程，包括：利用高斯混合模型（GMM）进行SOI状态估计、基于优化的装袋技术生成SOI、采用约束双向快速探索随机树（CBiRRT）进行SOI运动规划，以及通过模型预测控制（MPC）协调双臂操控。实验表明，该系统能够通过机械臂的自适应协调，实现对不同物体的精确、稳定装袋。所提出的框架推进了双臂机器人执行涉及可变形物体交互的常见任务中更复杂自动化的能力。