A deep understanding of kinematic structures and movable components is essential for enabling robots to manipulate objects and model their own articulated forms. Such understanding is captured through articulated objects, which are essential for tasks such as physical simulation, motion planning, and policy learning. However, creating these models, particularly for objects with high degrees of freedom (DoF), remains a significant challenge. Existing methods typically rely on motion sequences or strong assumptions from hand-curated datasets, which hinders scalability. In this paper, we introduce Kinematify, an automated framework that synthesizes articulated objects directly from arbitrary RGB images or textual descriptions. Our method addresses two core challenges: (i) inferring kinematic topologies for high-DoF objects and (ii) estimating joint parameters from static geometry. To achieve this, we combine MCTS search for structural inference with geometry-driven optimization for joint reasoning, producing physically consistent and functionally valid descriptions. We evaluate Kinematify on diverse inputs from both synthetic and real-world environments, demonstrating improvements in registration and kinematic topology accuracy over prior work.

翻译：对运动学结构和可动部件的深入理解对于机器人操控物体及建模自身铰接形态至关重要。此类理解通过铰接物体得以体现，其在物理仿真、运动规划与策略学习等任务中不可或缺。然而，创建此类模型（尤其是针对高自由度物体）仍面临重大挑战。现有方法通常依赖手工标注数据集中的运动序列或强假设，这限制了方法的可扩展性。本文提出Kinematify，一种能够直接从任意RGB图像或文本描述中合成铰接物体的自动化框架。我们的方法解决了两个核心挑战：（i）推断高自由度物体的运动学拓扑结构；（ii）从静态几何中估计关节参数。为实现这一目标，我们将用于结构推断的MCTS搜索与基于几何驱动的关节参数优化推理相结合，生成物理一致且功能有效的描述。我们在合成与真实环境中的多样化输入上评估Kinematify，结果表明其在配准精度与运动学拓扑准确性方面优于现有方法。