Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic Manipulation
This work addresses the challenge of accurate and efficient 3D modeling for markerless DLO manipulation in robotics, offering incremental improvements over existing data-driven methods.
The paper tackles the problem of modeling Deformable Linear Objects (DLOs) for robotic manipulation by proposing a Transformer-based model with a scaling method and data augmentation, achieving superior accuracy across different DLO lengths and enabling a simple MLP to reach near state-of-the-art performance with faster evaluation.
The robotic manipulation of Deformable Linear Objects (DLOs) is a vital and challenging task that is important in many practical applications. Classical model-based approaches to this problem require an accurate model to capture how robot motions affect the deformation of the DLO. Nowadays, data-driven models offer the best tradeoff between quality and computation time. This paper analyzes several learning-based 3D models of the DLO and proposes a new one based on the Transformer architecture that achieves superior accuracy, even on the DLOs of different lengths, thanks to the proposed scaling method. Moreover, we introduce a data augmentation technique, which improves the prediction performance of almost all considered DLO data-driven models. Thanks to this technique, even a simple Multilayer Perceptron (MLP) achieves close to state-of-the-art performance while being significantly faster to evaluate. In the experiments, we compare the performance of the learning-based 3D models of the DLO on several challenging datasets quantitatively and demonstrate their applicability in the task of shaping a DLO.