  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
