BagIt! An Adaptive Dual-Arm Manipulation of Fabric Bags for Object Bagging

Peng ZHOU, Jiaming Qi, hongmin wu, Chen Wang, CHEN YIZHOU, Zeqing Zhang

Attend In Person: Yes

Keywords: Manipulation, deformable object manipulation, dual arm manipulation

TL;DR: Accepted by IEEE Robotics and Automation Letters (2025)

Abstract: Bagging tasks, commonly found in industrial scenarios, are challenging considering the complicated and unpredictable nature of deformable bags. This paper presents an automated bagging system from the proposed adaptive Structure-of-Interest (SOI) manipulation strategy for dual robot arms. The system dynamically adjusts its actions based on real-time visual feedback, removing the need for pre-existing knowledge of bag properties. Our framework incorporates Gaussian Mixture Models (GMM) for estimating SOI states, optimization techniques for SOI generation, motion planning via Constrained Bidirectional Rapidly-exploring Random Tree (CBiRRT), and dual-arm coordination using Model Predictive Control (MPC). Extensive experiments validate the capability of our system to perform precise and robust bagging across various objects, showcasing its adaptability. This work offers a new solution for robotic deformable object manipulation (DOM), particularly in automated bagging tasks.

Submission Number: 12