用图神经网络学习液体与运动刚体的动态交互，可泛化到新物体和新任务。
Learning Particle Dynamics Subject to Rigid Body Manipulations Using Graph Neural Networks
- 以粒子为节点构建图结构，结合BVH算法处理粒子与运动刚体碰撞
- 在动态环境下精准模拟液体行为，且训练后能跨对象泛化
- 可作为可微分模拟器用于基于梯度的控制优化，适合机器人抓取等任务
高保真模拟粒子动力学对解决设计、图形学和机器人领域中涉及液体的交互与控制任务至关重要。近年来，基于图神经网络（GNN）的数据驱动方法在该领域取得进展，但通常局限于静态自由下落环境或简单几何体的操控，常忽略与动态运动刚体的复杂交互。本文提出一种从零开始设计的GNN框架，用于学习液体在刚体交互与主动操控下的动力学行为。粒子被表示为图节点，粒子-物体碰撞通过包围体积层次（BVH）算法与表面表示相结合处理。该方法在动态场景中准确捕捉流体行为，并可在静态自由下落环境中作为模拟器使用。尽管仅在单物体操控任务上训练，模型仍能有效泛化至新物体和新操控任务。最终，我们证明学习到的动力学可借助基于梯度的优化方法求解控制与操控任务。
原文摘要 · Abstract (English)
Simulating particle dynamics with high fidelity is crucial for solving real-world interaction and control tasks involving liquids in design, graphics, and robotics. Recently, data-driven approaches, particularly those based on graph neural networks (GNNs), have shown progress in tackling such problems. However, these approaches are often limited to learning fluid behavior in static free-fall environments or simple manipulation settings involving primitive objects, often overlooking complex interactions with dynamically moving kinematic rigid bodies. Here, we propose a GNN-based framework designed from the ground up to learn the dynamics of liquids under rigid body interactions and active manipulations, where particles are represented as graph nodes and particle-object collisions are handled using surface representations with the bounding volume hierarchy (BVH) algorithm. Our approach accurately captures fluid behavior in dynamic settings and can also function as a simulator in static free-fall environments. Despite being trained on single-object manipulation tasks, our model generalizes effectively to environments with novel objects and novel manipulation tasks. Finally, we show that the learned dynamics can be leveraged to solve control and manipulation tasks using gradient-based optimization methods.
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