Predictive models are a crucial component of many robotic systems. Yet, constructing accurate predictive models for a variety of deformable objects, especially those with unknown physical properties, remains a significant challenge. This paper introduces AdaptiGraph, a learning-based dynamics modeling approach that enables robots to predict, adapt to, and control a wide array of challenging deformable materials with unknown physical properties. AdaptiGraph leverages the highly flexible graph-based neural dynamics (GBND) framework, which represents material bits as particles and employs a graph neural network (GNN) to predict particle motion. Its key innovation is a unified physical property-conditioned GBND model capable of predicting the motions of diverse materials with varying physical properties without retraining. Upon encountering new materials during online deployment, AdaptiGraph utilizes a physical property optimization process for a few-shot adaptation of the model, enhancing its fit to the observed interaction data. The adapted models can precisely simulate the dynamics and predict the motion of various deformable materials, such as ropes, granular media, rigid boxes, and cloth, while adapting to different physical properties, including stiffness, granular size, and center of pressure. On prediction and manipulation tasks involving a diverse set of real-world deformable objects, our method exhibits superior prediction accuracy and task proficiency over non-material-conditioned and non-adaptive models. The project page is available at https://robopil.github.io/adaptigraph/ .

翻译：预测模型是众多机器人系统的关键组成部分。然而，为多种可变形物体（尤其是物理属性未知的物体）构建精确的预测模型仍然是一个重大挑战。本文提出AdaptiGraph，一种基于学习的动力学建模方法，使机器人能够预测、适应并控制多种物理属性未知的、具有挑战性的可变形材料。AdaptiGraph利用高度灵活的图基神经动力学（GBND）框架，将材料微元表示为粒子，并采用图神经网络（GNN）来预测粒子运动。其核心创新在于一个统一的物理属性条件化GBND模型，该模型能够预测具有不同物理属性的多种材料的运动，而无需重新训练。在在线部署过程中遇到新材料时，AdaptiGraph利用物理属性优化过程对模型进行少样本自适应，以增强其与观测交互数据的拟合度。自适应后的模型能够精确模拟多种可变形材料（如绳索、颗粒介质、刚性盒子和布料）的动力学行为并预测其运动，同时适应不同的物理属性，包括刚度、颗粒尺寸和压力中心。在涉及多种现实世界可变形物体的预测与操作任务中，我们的方法在预测精度和任务完成能力上均优于非物理属性条件化及非自适应模型。项目页面详见 https://robopil.github.io/adaptigraph/ 。