发表机构
University of Stuttgart（斯图加特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合双向长短期记忆网络建模与模型预测路径积分控制的机器学习方法，用于实现变形线性物体的精确高效操控，并通过仿真和实验验证了其有效性。
AI 中文摘要
变形线性物体（DLOs）如电缆的操控因其无限自由度和非线性动力学，对自动化构成了重大挑战。本文提出了一种基于机器学习的变形线性物体操控最优控制方法。该方法分为两个主要部分：建模和控制。对于DLO的动力学建模，我们提出了一种使用双向长短期记忆（biLSTM）网络的基于学习的方法。该biLSTM网络在由MuJoCo物理引擎生成的合成数据上进行训练。对于操控DLO，我们选择了一种采用模型预测路径积分（MPPI）控制的模型预测控制策略。所提出的方法通过仿真和实验进行了评估。结果表明，所提方法在实现DLO的精确和高效操控方面是有效的。
英文摘要
The manipulation of Deformable Linear Objects (DLOs) such as cables poses a significant challenge for automation due to their infinite degrees of freedom and non-linear dynamics. In this paper we present a machine learning based optimal control approach for the manipulation of DLOs. This approach is divided into two main components: modeling and control. For modeling the dynamics of the DLO, we propose a learning based approach using a bidirectional Long Short-Term Memory (biLSTM) network. The biLSTM network is trained on synthetic data generated by the MuJoCo physics engine. For manipulating the DLO, a model predictive control strategy that employs Model Predictive Path Integral (MPPI) control is selected. The proposed approach is evaluated through simulation and experiments. The results demonstrate the effectiveness of the proposed method in achieving accurate and efficient manipulation of DLOs.
Comments12 pages, 10 figures, 3 tables, 22nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2025)
Journal refProceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2025), Volume 1, pp. 47-58, SciTePress, 2025
