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 arms. 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：关节物体（如橱柜和门）在日常生活中广泛存在。然而，直接操作三维关节物体具有挑战性，因为它们具有多样的几何形状、语义类别和运动约束。以往的工作主要集中于识别和操作具有特定关节类型的关节物体，它们可以估计关节参数或区分合适的抓取姿态以辅助轨迹规划。尽管这些方法在特定类型的关节物体上取得了成功，但缺乏对未见物体的泛化能力，这严重阻碍了它们在更广泛场景中的应用。本文提出了一种新颖的可泛化关节物体关节建模与操作框架GAMMA（Generalizable Articulation Modeling and Manipulation for Articulated Objects），该框架能够从不同类别的多样关节物体中同时学习关节建模和抓取姿态赋能。此外，GAMMA采用自适应操作来迭代减少建模误差并提升操作性能。我们使用PartNet-Mobility数据集训练GAMMA，并在SAPIEN仿真环境和真实Franka机器人手臂上进行了综合实验评估。结果表明，GAMMA在未见及跨类别关节物体上显著优于最先进的关节建模和操作算法。我们将在最终版本中开源所有仿真和真实机器人的代码及数据集以支持复现。图片与视频已发布在项目网站：http://sites.google.com/view/gamma-articulation