Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects
  for Bimanual Robotic Manipulation
Piotr Kicki Michał Bidziński Krzysztof Walas
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.