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

Sim2Real$^2$: Actively Building Explicit Physics Model for Precise Articulated Object Manipulation