  Manipulating articulated objects requires multiple robot arms in general. It
is challenging to enable multiple robot arms to collaboratively complete
manipulation tasks on articulated objects. In this paper, we present
$\textbf{V-MAO}$, a framework for learning multi-arm manipulation of
articulated objects. Our framework includes a variational generative model that
learns contact point distribution over object rigid parts for each robot arm.
The training signal is obtained from interaction with the simulation
environment which is enabled by planning and a novel formulation of
object-centric control for articulated objects. We deploy our framework in a
customized MuJoCo simulation environment and demonstrate that our framework
achieves a high success rate on six different objects and two different robots.
We also show that generative modeling can effectively learn the contact point
distribution on articulated objects.
