  Virtualizing the physical world into virtual models has been a critical
technique for robot navigation and planning in the real world. To foster
manipulation with articulated objects in everyday life, this work explores
building articulation models of indoor scenes through a robot's purposeful
interactions in these scenes. Prior work on articulation reasoning primarily
focuses on siloed objects of limited categories. To extend to room-scale
environments, the robot has to efficiently and effectively explore a
large-scale 3D space, locate articulated objects, and infer their
articulations. We introduce an interactive perception approach to this task.
Our approach, named Ditto in the House, discovers possible articulated objects
through affordance prediction, interacts with these objects to produce
articulated motions, and infers the articulation properties from the visual
observations before and after each interaction. It tightly couples affordance
prediction and articulation inference to improve both tasks. We demonstrate the
effectiveness of our approach in both simulation and real-world scenes. Code
and additional results are available at
https://ut-austin-rpl.github.io/HouseDitto/
