  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.
