Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic Manipulation

• URL: http://arxiv.org/abs/2309.07609v1
• Date: Thu, 14 Sep 2023 11:17:43 GMT
• Title: Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic Manipulation
• Authors: Piotr Kicki, Micha{\l} Bidzi\'nski, Krzysztof Walas
• Abstract summary: This paper analyzes several learning-based 3D models of the Deformable Linear Objects (DLOs) We propose a new one based on the Transformer architecture that achieves superior accuracy, even on the DLOs of different lengths. We also introduce a data augmentation technique, which improves the prediction performance of almost all considered DLO data-driven models.
• Score: 6.212335606641129
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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