We address dynamic manipulation of deformable linear objects by presenting SPiD, a physics-informed self-supervised learning framework that couples an accurate deformable object model with an augmented self-supervised training strategy. On the modeling side, we extend a mass-spring model to more accurately capture object dynamics while remaining lightweight enough for high-throughput rollouts during self-supervised learning. On the learning side, we train a neural controller using a task-oriented cost, enabling end-to-end optimization through interaction with the differentiable object model. In addition, we propose a self-supervised DAgger variant that detects distribution shift during deployment and performs offline self-correction to further enhance robustness without expert supervision. We evaluate our method primarily on the rope stabilization task, where a robot must bring a swinging rope to rest as quickly and smoothly as possible. Extensive experiments in both simulation and the real world demonstrate that the proposed controller achieves fast and smooth rope stabilization, generalizing across unseen initial states, rope lengths, masses, non-uniform mass distributions, and external disturbances. Additionally, we develop an affordable markerless rope perception method and demonstrate that our controller maintains performance with noisy and low-frequency state updates. Furthermore, we demonstrate the generality of the framework by extending it to the rope trajectory tracking task. Overall, SPiD offers a data-efficient, robust, and physically grounded framework for dynamic manipulation of deformable linear objects, featuring strong sim-to-real generalization.

翻译：本文提出SPiD框架，一种融合精确可变形物体模型与增强型自监督训练策略的物理信息自监督学习框架，以解决可变形线性物体的动态操控问题。在建模方面，我们扩展了质点弹簧模型，使其在保持轻量化以适应自监督学习高通量推演的同时，更精确地捕捉物体动力学特性。在学习方面，我们采用面向任务的损失函数训练神经控制器，通过可微分物体模型的交互实现端到端优化。此外，我们提出一种自监督DAgger变体，能够在部署过程中检测分布偏移并执行离线自校正，从而在无需专家监督的情况下进一步提升鲁棒性。我们主要在绳索稳定任务上评估本方法，该任务要求机器人尽可能快速平稳地使摆动绳索静止。大量仿真与实物实验表明，所提出的控制器能实现快速平滑的绳索稳定，并泛化至未见过的初始状态、绳长、质量、非均匀质量分布及外部干扰。此外，我们开发了一种低成本的无标记绳索感知方法，证明控制器在噪声和低频状态更新下仍能保持性能。进一步地，我们通过将框架扩展至绳索轨迹跟踪任务，验证了其通用性。总体而言，SPiD为可变形线性物体的动态操控提供了一个数据高效、鲁棒且物理基础扎实的框架，并展现出优异的仿真到现实泛化能力。