用图神经网络学习张拉整体机器人的动态，提升模拟精度与效率。
Learning Differentiable Tensegrity Dynamics using Graph Neural Networks
- 基于张拉整体结构的图表示，用GNN建模缆索接触动力学。
- 在3杆和6杆仿真中逼近MuJoCo真实动力学，实测误差更低。
- 相比传统物理引擎和网格GNN方法，训练推理更快且更准。
张拉整体机器人由刚性杆和柔性缆索构成，是兼具刚柔特性的新型机器人系统，适用于移动与装配等多种场景。但由于其高自由度与柔性特性，精确建模与控制极具挑战。以往工作基于物理原理构建了可微分物理引擎，但本研究提出采用图神经网络（GNN）对张拉整体机器人的接触动力学进行建模，利用其自然的图结构——杆端节点间的缆索连接关系。该学习型模拟器在3杆与6杆张拉整体机器人的仿真-仿真实验中，以MuJoCo为真实基准，表现出高精度；在真实3杆机器人上（状态部分可观测）也优于此前的可微分引擎。相较于最新的基于网格的GNN模拟器，该方法在训练与推理阶段均更高效，同时精度更高。代码与数据已公开于https://github.com/nchen9191/tensegrity_gnn_simulator_public。
原文摘要 · Abstract (English)
Tensegrity robots are composed of rigid struts and flexible cables. They constitute an emerging class of hybrid rigid-soft robotic systems and are promising systems for a wide array of applications, ranging from locomotion to assembly. They are difficult to control and model accurately, however, due to their compliance and high number of degrees of freedom. To address this issue, prior work has introduced a differentiable physics engine designed for tensegrity robots based on first principles. In contrast, this work proposes the use of graph neural networks to model contact dynamics over a graph representation of tensegrity robots, which leverages their natural graph-like cable connectivity between end caps of rigid rods. This learned simulator can accurately model 3-bar and 6-bar tensegrity robot dynamics in simulation-to-simulation experiments where MuJoCo is used as the ground truth. It can also achieve higher accuracy than the previous differentiable engine for a real 3-bar tensegrity robot, for which the robot state is only partially observable. When compared against direct applications of recent mesh-based graph neural network simulators, the proposed approach is computationally more efficient, both for training and inference, while achieving higher accuracy. Code and data are available at https://github.com/nchen9191/tensegrity_gnn_simulator_public
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