  Accurately manipulating articulated objects is a challenging yet important
task for real robot applications. In this paper, we present a novel framework
called Sim2Real$^2$ to enable the robot to manipulate an unseen articulated
object to the desired state precisely in the real world with no human
demonstrations. We leverage recent advances in physics simulation and
learning-based perception to build the interactive explicit physics model of
the object and use it to plan a long-horizon manipulation trajectory to
accomplish the task. However, the interactive model cannot be correctly
estimated from a static observation. Therefore, we learn to predict the object
affordance from a single-frame point cloud, control the robot to actively
interact with the object with a one-step action, and capture another point
cloud. Further, the physics model is constructed from the two point clouds.
Experimental results show that our framework achieves about 70% manipulations
with <30% relative error for common articulated objects, and 30% manipulations
for difficult objects. Our proposed framework also enables advanced
manipulation strategies, such as manipulating with different tools. Code and
videos are available on our project webpage:
https://ttimelord.github.io/Sim2Real2-site/
