Robotic manipulation of deformable linear objects (DLOs) has broad application prospects in many fields. However, a key issue is to obtain the exact deformation models (i.e., how robot motion affects DLO deformation), which are hard to theoretically calculate and vary among different DLOs. Thus, shape control of DLOs is challenging, especially for large deformation control which requires global and more accurate models. In this paper, we propose a coupled offline and online data-driven method for efficiently learning a global deformation model, allowing for both accurate modeling through offline learning and further updating for new DLOs via online adaptation. Specifically, the model approximated by a neural network is first trained offline on random data, then seamlessly migrated to the online phase, and further updated online during actual manipulation. Several strategies are introduced to improve the model's efficiency and generalization ability. We propose a convex-optimization-based controller, and analyze the system's stability using the Lyapunov method. Detailed simulations and real-world experiments demonstrate that our method can efficiently and precisely estimate the deformation model, and achieve large deformation control of untrained DLOs in 2D and 3D dual-arm manipulation tasks better than the existing methods. It accomplishes all 24 tasks with different desired shapes on different DLOs in the real world, using only simulation data for the offline learning.
翻译:对变形线性天体(DLOs)的机械操纵在许多领域有着广泛的应用前景。然而,一个关键问题是获取精确的变形模型(即机器人运动如何影响DLO变形),这些模型很难在理论上计算,而且不同DLO之间也各不相同。因此,对DLO的形状控制具有挑战性,特别是对于大型变形控制来说,这需要全球和更精确的模型。在本文件中,我们提出了一个连接的离线和在线数据驱动方法,以便有效地学习一种全球变形模型,允许通过离线学习进行精确的建模,并通过在线适应进一步更新新的DLOs。具体地说,由神经网络近似于的模型首先通过随机数据进行离线培训,然后无缝地迁移到在线阶段,并在实际操作期间进一步更新在线。我们引入了几种战略来提高模型的效率和总体化能力。我们提出了一种基于 convex-opimimim化的控制器,并且仅使用Lyapunov 方法分析系统稳定性。详细的模拟和现实世界外实验表明,我们的方法可以高效和精确地对真实的变形模型进行估算,然后在不同的DLOD任务中实现大的变形,在不同的LOD的变形中,在不同的DLILOD上完成中可以改进所有不同的D的双重的双重任务中完成。