统一模拟刚体、柔体和关节体的物理动力学，让数字世界更真实可交互。
Generalized Dynamics Generation towards Scannable Physical World Model
- 从势能角度建模，用统一框架融合多种物理系统。
- 通过方向性刚度捕捉从软到硬的广泛物理行为。
- 适合开发可交互虚拟环境与复杂动态场景下的机器人训练。
具有真实交互动力学的数字孪生世界为在可扫描环境中开发通用具身智能体提供了新机遇。为此，我们提出GDGen（广义动力学生成的一般化表示），从势能视角将刚体、关节体和柔体动力学无缝整合进一个与几何无关的统一系统。该框架基于稳定物理系统的势能应保持较低的核心原理，将世界视为整体，仅通过简单运动观测即可推断底层物理属性。我们扩展经典弹性动力学，引入方向性刚度以覆盖软弹性、关节及刚体系统的行为。提出专用网络建模扩展材料属性，并采用神经场实现与几何无关的形变表示。大量实验表明，GDGen稳健统一了多种仿真范式，为构建交互式虚拟环境及在复杂动态场景中训练机器人提供了通用基础。
原文摘要 · Abstract (English)
Digital twin worlds with realistic interactive dynamics presents a new opportunity to develop generalist embodied agents in scannable environments with complex physical behaviors. To this end, we present GDGen (Generalized Representation for Generalized Dynamics Generation), a framework that takes a potential energy perspective to seamlessly integrate rigid body, articulated body, and soft body dynamics into a unified, geometry-agnostic system. GDGen operates from the governing principle that the potential energy for any stable physical system should be low. This fresh perspective allows us to treat the world as one holistic entity and infer underlying physical properties from simple motion observations. We extend classic elastodynamics by introducing directional stiffness to capture a broad spectrum of physical behaviors, covering soft elastic, articulated, and rigid body systems. We propose a specialized network to model the extended material property and employ a neural field to represent deformation in a geometry-agnostic manner. Extensive experiments demonstrate that GDGen robustly unifies diverse simulation paradigms, offering a versatile foundation for creating interactive virtual environments and training robotic agents in complex, dynamically rich scenarios.
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