Key Takeaways & Executive Findings
- •• A novel Physics-Informed Graph Neural Network (PIGNN) integrates physical laws into the loss function to accurately predict complex deformations of deformable linear objects (DLO) such as fibers, addressing nonlinear behaviors like bending and torsion. • The method leverages GNNs to learn an initial deformation model, mitigating data scarcity issues and improving prediction accuracy even with limited training data. • Experimental results show that PIGNN significantly reduces both execution time and prediction error compared to traditional methods, making it suitable for real-time robotic manipulation of DLO. • The approach demonstrates strong potential for applications in robotics, medical devices, aerospace, and manufacturing, particularly in tasks like wire assembly and surgical suturing.
Abstract
Shape prediction of deformable linear objects (DLO) plays critical roles in robotics, medical devices, aerospace, and manufacturing, especially in manipulating objects such as cables, wires, and fibers. Due to the inherent flexibility of DLO and their complex deformation behaviors, such as bending and torsion, it is challenging to predict their dynamic characteristics accurately. Although the traditional physical modeling method can simulate the complex deformation behavior of DLO, the calculation cost is high and it is difficult to meet the demand of real-time prediction. In addition, the scarcity of data resources also limits the prediction accuracy of existing models. To solve these problems, a method of fiber shape prediction based on a physical information graph neural network (PIGNN) is proposed in this paper. This method cleverly combines the powerful expressive power of graph neural networks with the strict constraints of physical laws. Specifically, we learn the initial deformation model of the fiber through graph neural networks (GNN) to provide a good initial estimate for the model, which helps alleviate the problem of data resource scarcity. During the training process, we incorporate the physical prior knowledge of the dynamic deformation of the fiber optics into the loss function as a constraint, which is then fed back to the network model. This ensures that the shape of the fiber optics gradually approaches the true target shape, effectively solving the complex nonlinear behavior prediction problem of deformable linear objects. Experimental results demonstrate that, compared to traditional methods, the proposed method significantly reduces execution time and prediction error when handling the complex deformations of deformable fibers. This showcases its potential application value and superiority in fiber manipulation.
1. Introduction
Deformable linear objects (DLO) refer to one-dimensional deformable entities, such as wires, ropes, and optical fibers. The shape prediction of DLO involves the predictive analysis of the deformation and motion of deformable objects with linear properties under external forces, which holds significant research value in fields such as robotics, medical devices, aerospace, and manufacturing. For instance, in wire manufacturing, shape prediction is utilized for the assembly of devices [1]; in surgical procedures, sutures are predicted in order to secure tissue together [2]. However, the inherent flexibility of DLO presents unique challenges for shape prediction. Different from rigid objects, flexible learning bodies may produce a variety of complex deformations, such as bending and twisting. Accurate prediction of the dynamic deformation of such objects requires the establishment of models that can predict complex deformation in real time, which makes this research field a key problem in robot control.
In shape prediction for DLO, a major challenge is the nonlinear deformation such as bending and torsion. When external conditions or constraints change, the DLO experiences complex nonlinear deformations. Because the traditional data-driven method [3–5] cannot fully consider the deformation characteristics of the DLO of materials such as optical fiber, it may make wrong prediction of the deformation of DLO, resulting in control failure and even damage to the DLO. Although traditional physical modeling methods such as finite element analysis [6–9] can simulate these complex phenomena, their computational costs are high and it is difficult to meet the needs of real-time control. Therefore, how to efficiently capture the complex nonlinear dynamics in fiber deformation has become an important topic to improve the accuracy and efficiency of the DLO.
Another challenge is the scarcity of data resources. Modeling of DLO requires a large amount of high-quality experimental data for training and verification, and the acquisition of these data is usually costly and time-consuming.
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Meixuan Wang, Junliang Wang, Jie Zhang, Xinting Liao, Guojin Li (2025). Physics-Informed Graph Learning for Shape Prediction in Robot Manipulate of Deformable Linear Objects. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01299-3
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a Physics-Informed Graph Neural Network (PIGNN) that integrates physical laws into the loss function to accurately predict the shape of deformable linear objects (DLO) like fibers, addressing challenges of nonlinear deformation and data scarcity.
How does PIGNN handle data scarcity?
PIGNN uses graph neural networks to learn an initial deformation model, providing a good initial estimate that helps alleviate the need for large amounts of training data.
What are the advantages of PIGNN over traditional methods?
PIGNN significantly reduces execution time and prediction error compared to traditional physical modeling and data-driven methods, making it suitable for real-time robotic manipulation of DLO.
In which applications can this method be used?
The method has potential applications in robotics, medical devices, aerospace, and manufacturing, such as wire assembly, surgical suturing, and manipulation of cables and fibers.
What types of deformations does the model predict?
The model predicts complex nonlinear deformations of DLO, including bending and torsion, which are common in flexible objects like wires and optical fibers.
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