JODA：可组合的联合动力学用于连杆物体
JODA: Composable Joint Dynamics for Articulated Objects
AI总结：
JODA通过结构化三通道场生成关节级动力学，结合视觉和语言模型推断并优化关节动力学，实现可控的连杆物体动力学建模。
摘要：
用于模拟和具身AI的连杆物体通常由几何和运动学结构指定，但缺乏控制真实机械行为的细粒度动力学效应，如摩擦保持、卡槽、软闭合和快扣。现有方法要么完全忽略动力学结构，要么使用表达力有限的简单模型。我们引入JODA，一种生成关节级动力学的框架，作为结构化的三通道场覆盖关节自由度，捕捉保守力、干摩擦和阻尼。通过形状约束的分段三次插值（PCHIP）实例化，该公式定义了一个紧凑且表达力强的功能空间，既可解释又兼容可微分模拟。基于此表示，我们开发了从多模态输入推断和优化关节动力学的方法。给定视觉观测和关节上下文，视觉-语言模型提出结构化的动力学原语，将其组合成统一的动力学场。所得到的表示支持直接操作和基于梯度的优化。我们证明JODA能够合理且可控地建模多样的关节行为，提供统一的接口用于推理、编辑和优化。代码和示例资产及其生成的轮廓将在发表时发布。
英文摘要：
Articulated objects used in simulation and embodied AI are typically specified by geometry and kinematic structure, but lack the fine-grained dynamical effects that govern realistic mechanical behavior, such as frictional holding, detents, soft closing, and snap latching. Existing approaches either ignore the detailed structure of dynamics entirely, or use simple models with limited expressiveness. We introduce JODA, a framework for generating joint-level dynamics as a structured three-channel field over the joint degree of freedom, capturing conservative forces, dry friction, and damping. Instantiated using shape-constrained piecewise cubic interpolation (PCHIP), this formulation defines a compact and expressive function space that is both interpretable and compatible with differentiable simulation. Building on this representation, we develop methods for inferring and refining joint dynamics from multimodal inputs. Given visual observations and joint context, a vision-language model proposes structured dynamical primitives, which are composed into a unified dynamics field. The resulting representation supports both direct manipulation and gradient-based refinement. We demonstrate that JODA enables plausible and controllable modeling of diverse joint behaviors, providing a unified interface for inference, editing, and optimization. Code and example assets with their generated profiles will be released upon publication.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
