Articulated objects like cabinets and doors are widespread in daily life. However, directly manipulating 3D articulated objects is challenging because they have diverse geometrical shapes, semantic categories, and kinetic constraints. Prior works mostly focused on recognizing and manipulating articulated objects with specific joint types. They can either estimate the joint parameters or distinguish suitable grasp poses to facilitate trajectory planning. Although these approaches have succeeded in certain types of articulated objects, they lack generalizability to unseen objects, which significantly impedes their application in broader scenarios. In this paper, we propose a novel framework of Generalizable Articulation Modeling and Manipulating for Articulated Objects (GAMMA), which learns both articulation modeling and grasp pose affordance from diverse articulated objects with different categories. In addition, GAMMA adopts adaptive manipulation to iteratively reduce the modeling errors and enhance manipulation performance. We train GAMMA with the PartNet-Mobility dataset and evaluate with comprehensive experiments in SAPIEN simulation and real-world Franka robot. Results show that GAMMA significantly outperforms SOTA articulation modeling and manipulation algorithms in unseen and cross-category articulated objects. We will open-source all codes and datasets in both simulation and real robots for reproduction in the final version. Images and videos are published on the project website at: http://sites.google.com/view/gamma-articulation

翻译：铰接物体（如橱柜和门）在日常生活中广泛存在。然而，直接操控三维铰接物体极具挑战性，因其几何形状、语义类别及动力学约束呈现多样性。现有研究大多集中于识别和操控具有特定关节类型的铰接物体，或通过估计关节参数，或通过区分适用抓取位姿以辅助轨迹规划。尽管这些方法在特定类型的铰接物体上取得了成功，但其对未见物体的泛化能力不足，严重阻碍了在更广泛场景中的应用。本文提出一种新颖的通用铰接建模与操控框架GAMMA，通过学习不同类别铰接物体的铰接建模及抓取位姿可供性，实现泛化。此外，GAMMA采用自适应操控策略，通过迭代建模误差以提升操控性能。我们基于PartNet-Mobility数据集训练GAMMA，并在SAPIEN仿真环境及真实Franka机器人上开展全面实验评估。结果表明，GAMMA在未见及跨类别铰接物体上显著优于现有最先进的铰接建模与操控算法。最终版本中，我们将开源所有仿真及真实机器人代码与数据集以供复现。相关图像与视频已发布于项目网站：http://sites.google.com/view/gamma-articulation