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具有挑战性，因为它们具有多样化的几何形状、语义类别和动力学约束。以往的工作主要集中在识别和操控具有特定关节类型的铰接物体，它们或能估计关节参数，或能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铰接物体上取得了成功，但它们缺乏对未见物体的泛化能力，这极大地阻碍了其在更广泛场景中的应用。本文提出了一种新颖的框架——面向铰接物体的泛化关节建模与操控（GAMMA），该框架从不同类别的多样化铰接物体中同时学习关节建模和抓取姿态启发性。此外，GAMMA采用自适应操控策略，通过迭代方式减少建模误差并提升操控性能。我们使用PartNet-Mobility数据集训练GAMMA，并在SAPIEN仿真环境和真实Franka机器人上通过全面实验进行评估。结果表明，GAMMA在未见及跨类别铰接物体的操控与关节建模任务上显著超越了当前最优算法。最终版本将开源仿真与真实机器人环境下的所有代码及数据集以供复现。相关图像和视频已发布在项目网站上：http://sites.google.com/view/gamma-articulation