This paper addresses the task of modeling Deformable Linear Objects (DLOs), such as ropes and cables, during dynamic motion over long time horizons. This task presents significant challenges due to the complex dynamics of DLOs. To address these challenges, this paper proposes differentiable Discrete Elastic Rods For deformable linear Objects with Real-time Modeling (DEFORM), a novel framework that combines a differentiable physics-based model with a learning framework to model DLOs accurately and in real-time. The performance of DEFORM is evaluated in an experimental setup involving two industrial robots and a variety of sensors. A comprehensive series of experiments demonstrate the efficacy of DEFORM in terms of accuracy, computational speed, and generalizability when compared to state-of-the-art alternatives. To further demonstrate the utility of DEFORM, this paper integrates it into a perception pipeline and illustrates its superior performance when compared to the state-of-the-art methods while tracking a DLO even in the presence of occlusions. Finally, this paper illustrates the superior performance of DEFORM when compared to state-of-the-art methods when it is applied to perform autonomous planning and control of DLOs.

翻译：本文针对可变形线性物体（如绳索和电缆）在长时间动态运动中的建模任务展开研究。由于可变形线性物体具有复杂的动力学特性，该任务面临重大挑战。为应对这些挑战，本文提出用于可变形线性物体实时建模的可微分离散弹性杆框架（DEFORM），该新型框架将基于可微分物理的模型与学习框架相结合，实现对可变形线性物体的精确实时建模。DEFORM的性能在包含两台工业机器人和多种传感器的实验装置中进行评估。通过一系列综合实验证明，与现有先进方法相比，DEFORM在精度、计算速度和泛化能力方面均表现出优越性能。为进一步展示DEFORM的实用性，本文将其集成到感知流程中，并证明即使在存在遮挡的情况下跟踪可变形线性物体时，其性能仍优于现有先进方法。最后，本文展示了DEFORM在应用于可变形线性物体的自主规划与控制任务时，相较于现有先进方法所展现的卓越性能。