Accurate prediction of deformable linear object (DLO) dynamics is challenging if the task at hand requires a human-interpretable yet computationally fast model. In this work, we draw inspiration from the pseudo-rigid body method (PRB) and model a DLO as a serial chain of rigid bodies whose internal state is unrolled through time by a dynamics network. This dynamics network is trained jointly with a physics-informed encoder which maps observed motion variables to the DLO's hidden state. To encourage that the state acquires a physically meaningful representation, we leverage the forward kinematics of the PRB model as decoder. We demonstrate in robot experiments that the proposed DLO dynamics model provides physically interpretable predictions from partial observations while being on par with black-box models regarding prediction accuracy. The project code is available at: http://tinyurl.com/prb-networks

翻译：对可变形线性物体（DLO）动力学的精确预测颇具挑战性，尤其是在需要兼具人类可解释性与计算效率的模型时。本文受伪刚体法（PRB）启发，将DLO建模为由刚体串联而成的链条，其内部状态通过动力学网络随时间展开。该动力学网络与基于物理信息的编码器联合训练，编码器将观测到的运动变量映射至DLO的隐藏状态。为促使状态获得具有物理意义的表征，我们利用PRB模型的正向运动学作为解码器。通过机器人实验表明，所提出的DLO动力学模型能从部分观测中提供物理可解释的预测，同时在预测精度上与黑箱模型相当。项目代码见：http://tinyurl.com/prb-networks