  In this paper, a feature extraction approach for the deformable linear object
is presented, which uses a Bezier curve to represent the original geometric
shape. The proposed extraction strategy is combined with a parameterization
technique, the goal is to compute the regression features from the
visual-feedback RGB image, and finally obtain the efficient shape feature in
the low-dimensional latent space. Existing works of literature often fail to
capture the complex characteristics in a unified framework. They also struggle
in scenarios where only local shape descriptors are used to guide the robot to
complete the manipulation. To address these challenges, we propose a feature
extraction technique using a parameterization approach to generate the
regression features, which leverages the power of the Bezier curve and linear
regression. The proposed extraction method effectively captures topological
features and node characteristics, making it well-suited for the deformation
object manipulation task. Large mount of simulations are conducted to evaluate
the presented method. Our results demonstrate that the proposed method
outperforms existing methods in terms of prediction accuracy, robustness, and
computational efficiency. Furthermore, our approach enables the extraction of
meaningful insights from the predicted links, thereby contributing to a better
understanding of the shape of the deformable linear objects. Overall, this work
represents a significant step forward in the use of Bezier curve for shape
representation.
