BagIt! An Adaptive Dual-Arm Manipulation of Fabric Bags for Object Bagging
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908533033795584 |
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| author | Zhou, Peng Qi, Jiaming Wu, Hongmin Wang, Chen Chen, Yizhou Zhang, Zeqing |
| author_facet | Zhou, Peng Qi, Jiaming Wu, Hongmin Wang, Chen Chen, Yizhou Zhang, Zeqing |
| contents | Bagging tasks, commonly found in industrial scenarios, are challenging considering deformable bags' complicated and unpredictable nature. 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. Video of this work is available at https://youtu.be/6JWjCOeTGiQ. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09484 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BagIt! An Adaptive Dual-Arm Manipulation of Fabric Bags for Object Bagging Zhou, Peng Qi, Jiaming Wu, Hongmin Wang, Chen Chen, Yizhou Zhang, Zeqing Robotics Systems and Control Bagging tasks, commonly found in industrial scenarios, are challenging considering deformable bags' complicated and unpredictable nature. 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. Video of this work is available at https://youtu.be/6JWjCOeTGiQ. |
| title | BagIt! An Adaptive Dual-Arm Manipulation of Fabric Bags for Object Bagging |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2509.09484 |