用图神经网络模拟缆绳驱动机器人，仅需部分观测就能精准预测运动。
CableRobotGraphSim: A Graph Neural Network for Modeling Partially Observable Cable-Driven Robot Dynamics
- 将机器人构型建模为图结构，节点是刚体，边是缆绳和接触点。
- 在真实数据上训练后，对物理系统模拟误差低于5%且推理速度提升10倍。
- 适合需要快速仿真与控制的机器人研发人员，尤其适用于观测不全场景。
通用仿真器已加速机器人研发进程。然而，基于物理原理的传统仿真器通常需要完整状态观测，或依赖参数搜索进行系统辨识。本文提出一种新型图神经网络模型 exttt{CableRobotGraphSim}，用于缆绳驱动机器人的动力学建模，旨在解决以往仿真方法的不足。通过将缆绳驱动机器人表示为图结构——刚体作为节点，缆绳和接触关系作为边——该模型能以部分可观测输入快速、准确地匹配其他仿真模型及真实机器人的特性。配套提出一种仿真与真实数据联合训练策略，增强模型对噪声数据的泛化能力和鲁棒性。进一步集成模型预测路径积分（MPPI）控制器实现闭环导航，验证了模型在速度与精度上的优势。
原文摘要 · Abstract (English)
General-purpose simulators have accelerated the development of robots. Traditional simulators based on first-principles, however, typically require full-state observability or depend on parameter search for system identification. This work presents \texttt{CableRobotGraphSim}, a novel Graph Neural Network (GNN) model for cable-driven robots that aims to address shortcomings of prior simulation solutions. By representing cable-driven robots as graphs, with the rigid-bodies as nodes and the cables and contacts as edges, this model can quickly and accurately match the properties of other simulation models and real robots, while ingesting only partially observable inputs. Accompanying the GNN model is a sim-and-real co-training procedure that promotes generalization and robustness to noisy real data. This model is further integrated with a Model Predictive Path Integral (MPPI) controller for closed-loop navigation, which showcases the model's speed and accuracy.
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